Reproducible analysis

Introduction

The BlantyreSepsis R package contains contains data code to replicate the analysis of the manuscript:

A longitudinal observational study of aetiology and long-term outcomes of sepsis in Malawi revealing the key role of disseminated tuberculosis

Joseph M Lewis^1,2,3, , Madalitso Mphasa¹, Lucy Keyala¹, Rachel Banda¹, Emma Smith¹,², Jackie Duggan⁴, Tim Brooks⁴, Matthew Catton⁴, Jane Mallewa⁵, Grace Katha⁵, Stephen B Gordon^1,2, Brian Faragher², Melita A Gordon^1,3, Jamie Rylance^1,2, Nicholas A Feasey^1,2

Malawi Liverpool Wellcome Clinical Research Programme, Blantyre, Malawi
Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, UK
Department of Clinical Infection, Microbiology and Immunology, University of Liverpool, Liverpool, UK
Rare and Imported Pathogens Laboratory, Public Health England, UK
College of Medicine, University of Malawi ,Malawi

Now published in Clinical Infectious Diseases here

Installing and accessing data

Install the package from GitHub:

install.packages("devtools")
devtools::install_github("https://github.com/joelewis101/blantyreSepsis")

Or check out the source code at GitHub

Six datasets are included in the package; for details and variable definitions access the associated help file (e.g via ?BTparticipants). They are lazy loaded so will be available on loading the package.

BTparticipants - Baseline characteristics and investigation results for included participants
BTtreatments - Antimicrobial therapy and intravenous fluid therapy received by included participants
BTaetiology - Results of malaria testing, aerobic blood culture, cerebrospinalfluid culture, tuberculosis diagnostic tests
BTsera - Results of serologic tests for arboviruses, Rickettsioses, Leptospirosis
BTarraycard - Results of multiplex PCR test for high consequence pathogens
BTbc - Results of aerobic blood culture

This analysis is available as a package vignette; this can be built when downloading the package by typing:

devtools::install_github("https://github.com/joelewis101/blantyreSepsis", build_vignettes = TRUE )

This vignette will be then be available by typing vignette("analysis") and the code is accessed by typing edit(vigentte("analysis")). Be warned: building the vignette takes some time as it will run through the full missing data imputation and model fitting!

Alternatively the source code for the vignette is analysis.Rmd in the vignettes/ folder of this repo or the pkgdown site for this package has a rendered version, as well as variable definitions for the datasets.

Descriptive analysis

Setup and load packages

library(survival)
library(blantyreSepsis)
library(dplyr)
library(tidyr)
library(stringr)
library(forcats)
library(purrr)
library(kableExtra)
library(ggplotify)
library(UpSetR)
library(eulerr)
library(patchwork)
library(survminer)
library(wBoot)
library(viridis)
library(brms)
library(pheatmap)
library(factoextra)
library(mice)
library(bayesplot)
library(splines)
library(here)

# flag for savingfigures

write_figs <- FALSE

if (write_figs) {
  dir.create(here("figures"))
  dir.create(here("figures/upset_fig_files"))
}
#function for bootstrapping differnce in proportions

propz <- function(dat) {
  out <- sum(dat == 1, na.rm = TRUE) / length(dat)
  return(out)
}

Demographics of included patients


# Table of demographics

BTparticipants %>% 
  mutate(
    gcs_lessthan_15 = if_else(gcs < 15, true =  "yes", false = "no"),
  ) %>% 
  select(
    calc_age,
    ptsex,
    hivstatus,
    cd4_absolute, 
    hivonart,  
    art_time, 
    hivcpt,
    ever_tb, 
    tbongoing,
    screentemp,
    t0hr,
    t0rr,
    t0sbp, 
    t0dbp, 
    t0spo2, 
    gcs_lessthan_15, 
    ustand,
    days_unwell,
    haemoglobin,
    platelets, 
    wcc, 
    sodium,
    potassium, 
    co2, 
    creatinine, 
    lactate
    ) %>% 
  do(pretty_tbl_df(.,vars_to_char = c("ustand"),
                   vars_to_specify_rounding = c(
                     "screentemp" = 1,
                     "haemoglobin" = 1,
                     "potassium" = 1,
                     "lactate" = 1),
                   confint = FALSE)) %>%
  filter(!levels %in% c("Non reactive",
                        "No",
                        "no",
                        "0",
                        "Female")) %>%
  mutate(levels = case_when(
    variable == "calc_age" ~ "Age (years), median (IQR)",
    variable == "ptsex" ~ "Male sex n/N (%)",
    variable == "hivstatus" ~ "HIV infected*, n/N (%)",
    variable == "hivonart" ~ "Receiving antiretroviral therapy, n/N (%)",
    variable == "cd4_Absolute" ~ "CD4 lymphocyte count (10^6^/L), median (IQR)",
    variable == "art_time" ~ 
      "Time on antiretroviral therapy (months), median (IQR)",
    variable == "hivcpt" ~ 
      "Receiving co-trimoxazole preventative therapy, n/N (%)",
    variable == "ever_tb" ~ "History of receiving TB treatment n/N (%)",
    variable == "tbongoing" ~ 
      "Of those, currently receiving TB treatment n/N (%)",
    variable == "screentemp" ~ "Temperature (C), median (IQR)",
    variable == "t0hr" ~ "Heart rate (beats/min), median (IQR)",
    variable == "t0rr" ~ "Respiratory rate (breaths/min), median (IQR)",
    variable == "t0sbp" ~ "Systolic blood pressure (mmHg), median (IQR)", 
    variable == "t0dbp" ~ "Diastolic blood pressure (mmHg), median (IQR)", 
    variable == "t0spo2" ~ "Oxygen saturation (%), median (IQR)",
    variable == "gcs_lessthan_15" ~ "Glasgow coma score < 15 n/N (%)",
    variable ==  "ustand" ~ "Unable to stand unaided n/N (%)",
    variable ==  "days_unwell" ~ 
      "Length of time unwell for (days), median (IQR)", 
    variable ==  "haemoglobin" ~ "Haemoglobin (g/dL), median (IQR)",
    variable ==  "platelets" ~ "platelets (10^9^/l), median (IQR)",
    variable ==  "wcc" ~ "White cell count (10^9^/l), median (IQR)",
    variable ==  "sodium" ~ "Sodium (mmol/L), median (IQR)", 
    variable ==  "potassium" ~ "Potassium (mmol/L), median (IQR)", 
    variable ==  "co2" ~ "Bicarbonate (mmol/L), median (IQR)", 
    variable ==  "creatinine" ~ "Creatinine (mmol/L), median (IQR)", 
    variable ==  "lactate" ~ "Lactate (mmol/L), median (IQR)", 
    TRUE ~ levels),
    
    variable = case_when(
      variable %in% c("calc_age",
                      "ptsex") ~ "Demographics",
      variable %in% c("hivstatus",
                      "cd4_absolute",
                      "hivonart",
                      "art_time",
                      "hivcpt",
                      "ever_tb",
                      "tbongoing") ~ "HIV/TB status",
      str_detect(variable, "t0") | 
        variable %in% c("screentemp",
                        "gcs_lessthan_15",
                        "ustand",
                        "days_unwell") ~ "Physiology",
      variable %in% c("haemoglobin",
                      "platelets",
                      "wcc",
                      "sodium",
                      "potassium",
                      "co2",
                      "creatinine",
                      "lactate") ~ "Laboratory parameters"
    )
  ) -> t1

t1 %>%
  select(-variable) %>%
  kbl(col.names = c("Variable", "Value"),
   caption = "TABLE 2: baseline characteristics of included participants") %>%
  kable_classic(full_width = FALSE) %>%
  pack_rows(index = make_kable_rowgroup_string(t1, variable)) %>% 
  footnote(symbol  = c("HIV status missing for 12 participants"))

TABLE 2: baseline characteristics of included participants
Variable	Value
Demographics
Age (years), median (IQR)	36 (28-44)
Male sex n/N (%)	114/225 (51%)
HIV/TB status
HIV infected*, n/N (%)	143/213 (67%)
Median (IQR)	156 (51-298)
Receiving antiretroviral therapy, n/N (%)	117/143 (82%)
Time on antiretroviral therapy (months), median (IQR)	29 (4-73)
Receiving co-trimoxazole preventative therapy, n/N (%)	98/141 (70%)
History of receiving TB treatment n/N (%)	37/225 (16%)
Of those, currently receiving TB treatment n/N (%)	10/37 (27%)
Physiology
Temperature (C), median (IQR)	38.5 (37.9-39.0)
Heart rate (beats/min), median (IQR)	121 (102-132)
Respiratory rate (breaths/min), median (IQR)	34 (32-38)
Systolic blood pressure (mmHg), median (IQR)	99 (85-119)
Diastolic blood pressure (mmHg), median (IQR)	66 (57-76)
Oxygen saturation (%), median (IQR)	96 (94-98)
Glasgow coma score < 15 n/N (%)	21/225 (9%)
Unable to stand unaided n/N (%)	63/225 (28%)
Length of time unwell for (days), median (IQR)	7 (3-14)
Laboratory parameters
Haemoglobin (g/dL), median (IQR)	10.8 (8.2-13.2)
platelets (10⁹/l), median (IQR)	218 (146-297)
White cell count (10⁹/l), median (IQR)	6 (4-11)
Sodium (mmol/L), median (IQR)	134 (130-137)
Potassium (mmol/L), median (IQR)	4.0 (3.6-4.4)
Bicarbonate (mmol/L), median (IQR)	19 (17-22)
Creatinine (mmol/L), median (IQR)	76 (59-103)
Lactate (mmol/L), median (IQR)	3.4 (2.3-5.2)
^* HIV status missing for 12 participants




# Table of differences in variables  between hiv or not hiv
# expressed as bootstrapped difference in expressed as median or proportion



BTparticipants %>% 
  mutate(
    gcs_lessthan_15 = if_else(gcs < 15, true =  "yes", false = "no"),
    ptsex = if_else(ptsex == "Male", "1", "0"),
    ustand = as.character(ustand)
  ) %>% 
  select(
    pid,
    calc_age,
    ptsex,
    hivstatus,
    ever_tb, 
    screentemp,
    t0hr,
    t0rr,
    t0sbp, 
    t0dbp, 
    t0spo2, 
    gcs_lessthan_15, 
    ustand,
    days_unwell,
    haemoglobin,
    platelets, 
    wcc, 
    sodium,
    potassium, 
    co2, 
    creatinine, 
    lactate
    ) %>% 
  filter(!is.na(hivstatus)) ->
  df.sum.tbl

# bootstrap differences in proportions for character vars


left_join(
  df.sum.tbl %>%
    select(where(is.character)) %>%
    select(-pid) %>%
    pivot_longer(-hivstatus) %>%
    dplyr::group_by(name, hivstatus) %>%
    summarise(str = paste0(
      sum(grepl("1|yes", value, ignore.case = TRUE) , na.rm = TRUE),
      " (",
      sp_dc(100 * sum(grepl("1|yes", value, ignore.case = TRUE)) /
              sum(!is.na(value)), 0),
      "%)"
    )) %>%
    pivot_wider(
      id_cols = "name",
      names_from = "hivstatus",
      values_from = "str"
    ),
  
  df.sum.tbl %>%
    select(where(is.character)) %>%
    select(-pid) %>%
    pivot_longer(-hivstatus) %>%
    mutate(value = if_else(grepl("1|yes", value, ignore.case = TRUE),
                           "1",
                           "0")) %>% 
    dplyr::group_by(name) %>%
    dplyr::summarise(bs = list(boot.two.bca(value,
                                            hivstatus,
                                            propz))) %>%
    mutate(
      diff = map_dbl(bs, "Observed"),
      diff.lci = map_dbl(bs,
                         function(x)
                           unlist((x["Confidence.limits"][[1]][[1]]))),
      diff.uci = map_dbl(bs,
                         function(x)
                           unlist((x["Confidence.limits"][[1]][[2]])))
    ) %>%
    select(-bs) %>%
    mutate(diff.str = paste0(
      sp_dc(diff * 100, 0),
      "% (",
      sp_dc(diff.lci * 100, 0),
      " to ",
      sp_dc(diff.uci * 100, 0),
      "%)"
    )) ,
  by = "name"
) ->  diff.in.prop.cat.vars
#> `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.

# bootstrap differences in medians for numeric vars

df.sum.tbl %>% 
  select(where(is.numeric) | ends_with("hivstatus")) %>% 
  filter(!is.na(hivstatus)) %>%  
  pivot_longer(-hivstatus) %>%
  filter(!is.na(hivstatus) & !is.na(value)) %>% 
  dplyr::group_by(name) %>%
  dplyr::summarise(bs = list(boot.two.per(value, hivstatus, median))) %>%
  mutate(diff = map_dbl(bs, "Observed"),
         diff.lci = map_dbl(bs, 
                            function(x) 
                              unlist((x["Confidence.limits"][[1]][[1]]))),
          diff.uci = map_dbl(bs, 
                             function(x) 
                               unlist((x["Confidence.limits"][[1]][[2]])))) %>% 
  select(-bs) %>% 
  mutate(diff.str = paste0(sp_dc(diff,1),
                           " (", 
                           sp_dc(diff.lci,1),
                           " to ",
                           sp_dc(diff.uci,1),
                           ")"
                           )
  ) -> diff.in.median.num.vars


#summary stats for tb and not tb groups

df.sum.tbl %>% 
  select(where(is.numeric) | ends_with("hivstatus")) %>%
  pivot_longer(-hivstatus) %>% filter(!is.na(hivstatus)) %>%
  dplyr::group_by(name, hivstatus) %>% 
  dplyr::summarise( median = median(value, na.rm = T),
                    LQ = quantile(value, 0.25, na.rm = T),
                    UQ = quantile(value, 0.75, na.rm = T)) %>% 
  mutate(
    med_str = paste0(
      sp_dc(median, 1), " (",
      sp_dc(LQ, 1), "-",
      sp_dc(UQ, 1) ,")")
  ) %>% 
  select(name, hivstatus, med_str) %>% 
  pivot_wider(names_from = hivstatus, values_from = med_str) -> 
  summ.stats.by.group
#> `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.


st_t2_hivstrat <- merge(summ.stats.by.group, diff.in.median.num.vars, all.x = T)

st_t2_hivstrat  <- bind_rows(st_t2_hivstrat,diff.in.prop.cat.vars)


st_t2_hivstrat$boldcol <- FALSE 
st_t2_hivstrat$boldcol[(st_t2_hivstrat$diff.lci >= 0 &
                st_t2_hivstrat$diff.uci > 0) | (st_t2_hivstrat$diff.lci < 0 & 
                                        st_t2_hivstrat$diff.uci <= 0)] <- TRUE




st_t2_hivstrat[match(
  c(
    "calc_age",
    "ptsex",
    "days_unwell",
    "ever_tb",
    "screentemp",
    "t0hr",
    "t0sbp",
    "t0dbp",
    "t0rr",
    "t0spo2",
    "gcs_lessthan_15",
    "ustand",
    "haemoglobin",
    "wcc",
    "platelets",
    "sodium",
    "co2",
    "creatinine",
    "lactate"
  ),
  st_t2_hivstrat$name
), ] -> st_t2_hivstrat



st_t2_hivstrat <- st_t2_hivstrat[c("name", "Reactive", "Non reactive", "diff.str", "boldcol")]
names(st_t2_hivstrat) <- c("Variable", "HIV infected", "HIV uninfected", "Difference", "boldcol")

boldrows <- which(st_t2_hivstrat$boldcol %in% TRUE)

st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "calc_age"] <- "Age (years)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "ptsex"] <- "Male sex"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "hivstatus"] <- "HIV Infected"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "haemoglobin"] <- "Haemoglobin (x10^9^} g/dL)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "cd4_absolute"] <- "CD4 count 10^6^ /L"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "screentemp"] <- "Temperature (C)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "ever_tb"] <- "Previous or current TB"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "t0hr"] <- "Heart rate (beats/min)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "t0sbp"] <- "Systolic BP (mmHg)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "t0dbp"] <- "Diastolic BP (mmHg)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "t0rr"] <- "Respiratory rate (breaths/min)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "t0spo2"] <- "Oxygen saturation (%)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "gcs_lessthan_15"] <- "GCS below 15"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "ustand"] <- "Unable to stand"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "lactate"] <- "Lactate (mmol/L)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "wcc"] <- "White cell count (x10^9^)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "platelets"] <- "Platelet count (x10^9^ /L)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "sodium"] <- "Sodium (mmol /L)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "co2"] <- "Bicarbonate (mmol /L)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "urea"] <- "Urea (mmol /L)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "creatinine"] <- "Creatinine (mmol /L)"
st_t2_hivstrat$Variable[st_t2_hivstrat$Variable == "days_unwell"] <- "Length of time unwell for (days)"



kbl(select(st_t2_hivstrat,-boldcol), 
      row.names = F, caption = "SUPPLEMENTARY TABLE 2: Univariable associations with HIV status. Numeric variables are presented as median (IQR) and categorical variables as proportions. Difference column shows difference in medians or difference in proportions with 95% confidence intervals. Variables shown in bold are those for which the 95% confidence intervals do not cross 0." ) %>%
 kable_classic(full_width = FALSE) %>%
  row_spec(boldrows, bold = T)

SUPPLEMENTARY TABLE 2: Univariable associations with HIV status. Numeric variables are presented as median (IQR) and categorical variables as proportions. Difference column shows difference in medians or difference in proportions with 95% confidence intervals. Variables shown in bold are those for which the 95% confidence intervals do not cross 0.
Variable	HIV infected	HIV uninfected	Difference
Age (years)	38.0 (32.3-43.9)	32.0 (24.0-39.6)	-6.0 (-11.7 to -2.9)
Male sex	62 (43%)	44 (63%)	20% (5 to 33%)
Length of time unwell for (days)	10.0 (4.0-21.0)	6.5 (3.0-14.0)	-3.5 (-10.0 to 0.0)
Previous or current TB	35 (24%)	2 (3%)	-22% (-30 to -14%)
Temperature (C)	38.4 (37.9-39.0)	38.5 (38.0-39.2)	0.1 (-0.1 to 0.4)
Heart rate (beats/min)	124.0 (110.0-134.0)	111.5 (98.0-129.2)	-12.5 (-22.0 to -6.0)
Systolic BP (mmHg)	95.0 (83.5-113.0)	104.0 (92.8-123.0)	9.0 (1.5 to 25.5)
Diastolic BP (mmHg)	65.0 (55.0-72.0)	72.0 (62.2-84.8)	7.0 (1.0 to 11.0)
Respiratory rate (breaths/min)	34.0 (32.0-38.0)	34.0 (32.0-36.0)	0.0 (-1.5 to 2.0)
Oxygen saturation (%)	97.0 (94.0-98.0)	96.0 (95.0-98.0)	-1.0 (-1.0 to 1.0)
GCS below 15	13 (9%)	7 (10%)	1% (-7 to 10%)
Unable to stand	44 (31%)	12 (17%)	-14% (-25 to -2%)
Haemoglobin (x10⁹} g/dL)	10.0 (7.6-11.6)	13.3 (10.9-15.0)	3.3 (2.1 to 4.2)
White cell count (x10⁹)	6.4 (4.2-11.8)	6.7 (4.6-11.0)	0.2 (-1.3 to 1.9)
Platelet count (x10⁹ /L)	230.0 (156.0-314.0)	202.0 (125.8-288.0)	-28.0 (-78.0 to 6.0)
Sodium (mmol /L)	133.0 (128.0-136.0)	135.0 (133.0-138.0)	2.0 (0.5 to 4.0)
Bicarbonate (mmol /L)	19.0 (16.0-21.0)	21.5 (18.2-24.0)	2.5 (1.0 to 4.0)
Creatinine (mmol /L)	75.0 (58.0-105.0)	73.5 (60.2-95.0)	-1.5 (-9.0 to 12.0)
Lactate (mmol/L)	3.7 (2.4-5.2)	3.1 (2.1-5.0)	-0.6 (-1.0 to 0.1)

Treatments administered


# Table of treatments administered

BTtreatment %>% 
  select(-c(pid, iv_fluid_6hr)) %>% 
  mutate(ab = if_else(is.na(which_ab), "no", "antibacterial"),
         antifungal = if_else(is.na(which_antifungal), "no",
                              "antifungal"),
         antimalarial = if_else(is.na(which_antimalarial), "no",
                                "antimalarial"),
         antitb = if_else(is.na(which_antitb), "no",
                                "antitb")) %>% 
  pretty_tbl_df(vars_to_specify_rounding = c("timeto_ab" = 1,
                                             "timeto_antifungal" = 1,
                                             "timeto_antimalarial" = 1,
                                             "timeto_antitb" = 1),
                                             confint = FALSE) %>% 
  mutate(variable = gsub("timeto_", "", variable)) %>% 
  filter(levels != "no") -> t2

left_join(
  filter(t2, levels != "Median (IQR)"),
  t2 %>% 
    filter(levels == "Median (IQR)") %>% 
    select(-levels),
  by = "variable"
) -> t2

t2$variable <- factor(t2$variable, 
                         levels = c("ab", "which_ab",
                                    "antitb", "which_antitb",
                                    "antifungal", "which_antifungal",
                                    "antimalarial", "which_antimalarial"))

t2[order(t2$variable),] -> t2
indentrows <- which(grepl("which", t2$variable))

t2 %>%
  mutate(levels = case_when(
    levels == "antitb" ~ "Antitubercular",
    levels == "RHZE" ~ "RHZE",
    levels == "Lumefantrine-Artemether" ~ "Lumefantrine-Artemether",
    TRUE ~ str_to_title(levels)),
    value.y = if_else(is.na(value.y), "", value.y)
    ) %>% 
    select(-variable) %>% 
  kbl(caption = "SUPPLEMENTARY TABLE 3: Antimicroial therapies administered.",
      col.names = c("Antimicrobial therapy", 
                    "No. (proportion [95% CI]) participants", 
                    "Median (IQR) hours to administration"),
      row.names = FALSE,
      ) %>%
    kable_classic(full_width = FALSE) %>%
    add_indent(indentrows, all_cols = TRUE) %>%
    row_spec(indentrows, italic = TRUE) %>% 
    footnote(
      general = str_wrap(paste0(
        "RHZE = standard antituberculous chemotherapy: ",
        "Rifampicin (R), Isoniaid (H), Pyrazinamide (Z) ",
        "and Ethambutol (E)")), 
      symbol = str_wrap(paste0(
      "10/63 participants who received antitubercular ", 
      "agents during admission were taking them prior to ", 
      "admission; they are excluded from the ", 
      "calculation of median door-to-antimicrobial ", 
      "time for this class. "),80),
             footnote_as_chunk = TRUE)

SUPPLEMENTARY TABLE 3: Antimicroial therapies administered.
Antimicrobial therapy	No. (proportion [95% CI]) participants	Median (IQR) hours to administration
Antibacterial	207/225 (92%)	5.3 (3.7-10.8)
Ceftriaxone	181/207 (87%)
Ciprofloxacin	18/207 (9%)
Amoxicillin	5/207 (2%)
Metronidazole	2/207 (1%)
Co-Amoxiclav	1/207 (0%)
Antitubercular	53/225 (24%)	120.9 (63.7-171.0)
RHZE	53/53 (100%)
Antifungal	26/225 (12%)	47.7 (27.9-73.9)
Fluconazole	25/26 (96%)
Amphotericin	1/26 (4%)
Antimalarial	12/225 (5%)	4.5 (3.1-21.7)
Artesunate	11/12 (92%)
Lumefantrine-Artemether	1/12 (8%)
Note: RHZE = standard antituberculous chemotherapy: Rifampicin (R), Isoniaid (H), Pyrazinamide (Z) and Ethambutol (E)
^* 10/63 participants who received antitubercular agents during admission were taking them prior to admission; they are excluded from the calculation of median door-to-antimicrobial time for this class.

Aetiology


# Table of aetiology

BTaetiology %>% 
  select(-c(`mtb bsi`,uLAM, Xpert,bact_target_pcr, CrAg_LFA)) %>%  
  pivot_longer(-c(pid,hivstatus)) %>%
  dplyr::group_by(name) %>%
   dplyr::summarise(n_pos = sum(value == 1, na.rm = TRUE),
            n_neg = sum(value == 0 , na.rm = TRUE),
            n_positive_tests = n_pos/(n_pos + n_neg),
            n_positive_tests_lci = binom.test(n_pos, n_pos + n_neg)$conf.int[1],
            n_positive_tests_uci = binom.test(n_pos, n_pos + n_neg)$conf.int[2],
            
            positive_tests = paste0(n_pos,"/", n_pos + n_neg),
            positive_tests_prop = paste0(sp_dc(n_positive_tests*100,1), "%"),
            positive_tests_prop_ci = paste0(
              "(",
              sp_dc(n_positive_tests_lci * 100, 1),
              "-",
              sp_dc(n_positive_tests_uci *
                      100, 1),
              "%)"
            ), 
            n_prev = n_pos / 225,
            n_prev_lci = binom.test(n_pos, 225)$conf.int[1],
            n_prev_uci = binom.test(n_pos, 225)$conf.int[2], 
            prevalence = paste0(n_pos,"/225"),
            prevalence_prop = paste0("",sp_dc(n_prev*100,1), "%"),
            prevalence_prop_ci = paste0("(", sp_dc(n_prev_lci*100,1), "-",
                                sp_dc(n_prev_uci*100,1),"%)"
                                )
   ) %>% ungroup() %>% 
    # arrange((fct_relevel(name, 
    # "tb",
    # "bsi.bacterial","csf.bacterial","inv.bacterial",
    # "chik", "dengue","arbovirus",
    # "malaria",
    # "bsi.fungal","csf.fungal","inv.fungal",
    # "SF", "ET","ricks", 
    # "lepto", "borrelia", "rift_valley_fever"))) %>%
  arrange((fct_relevel(name, 
    "tb",
    "bsi.bacterial","csf.bacterial","inv.bacterial",
    "chik", "dengue","arbovirus",
    "malaria",
    "bsi.fungal","csf.fungal","inv.fungal",
    "SF", "ET","ricks", 
    "lepto", "borrelia", "rift_valley_fever"))) %>%
  mutate(across(contains("prevalence"),  
                ~ if_else(name %in% c("bsi.bacterial",
                                      "csf.bacterial",
                                      "chik",
                                      "dengue",
                                      "bsi.fungal",
                                      "csf.fungal",
                                      "SF",
                                      "ET"), 
                          "",
                          .)
  )) %>% 
  mutate(name = recode(name,
                "tb" = "Tuberculosis",
                "bsi.bacterial" = "Bloodstream infection",
                "csf.bacterial" = "Meningitis",
                "inv.bacterial" = "Any invasive bacterial infection",
                "bsi.fungal" = "Bloodstream infection",
                "csf.fungal" = "Meningitis",
                "inv.fungal" = "Any invasive fungal infection",
                "chik" = "Chikungunya",
                "dengue" = "Dengue",
                "arbovirus" = "Any arbovirus infection",
                "malaria" = "Falciparum malaria",
                "SF" = "Spotted fever group",
                "ET" = "Epidemic typhus group",
                "ricks" = "Total",
                "lepto" = "Leptospirosis",
                "borrelia" = "Borreliosis",
                "rift_valley_fever" = "Rift Valley fever",
                )) %>%
  dplyr::select(name, positive_tests, positive_tests_prop,positive_tests_prop_ci, 
         prevalence, prevalence_prop, prevalence_prop_ci) %>% 
  kbl(caption = 
        "TABLE 3: Diagnoses in study participants and proportion of participants with positive results.",
      col.names = c("Diagnosis", 
                    "n/N",
                    "%",
                    "(95% CI)",
                    "n/N",
                    "%",
                    "95% CI"),
    row.names = FALSE,
    ) %>%
  kable_classic(full_width = FALSE) %>% 
  #add_indent(c(1,4,7,10,11,14,15,16,17), level_of_indent = -1) %>%
 # row_spec(c(2,3,5,6,9,10,12,13), italic = TRUE) %>%
  add_header_above(c(" ", "Proportion of paricipants with positive result" = 3,
                     "Cohort prevalence" = 3)) %>% 
  pack_rows("Tuberculosis",1,1) %>%
  pack_rows("Invasive bacterial infection",2,4) %>%
  pack_rows("Arbovirus", 5,7) %>% 
  pack_rows("Malaria", 8,8) %>% 
  pack_rows("Invasive fungal infection",9,11) %>%
  pack_rows("Rickettsioses", 12,14) %>%
  pack_rows("Other", 15,17)

TABLE 3: Diagnoses in study participants and proportion of participants with positive results.
	Proportion of paricipants with positive result			Cohort prevalence
Diagnosis	n/N	%	(95% CI)	n/N	%	95% CI
Tuberculosis
Tuberculosis	76/162	46.9%	(39.0-54.9%)	76/225	33.8%	(27.6-40.4%)
Invasive bacterial infection
Bloodstream infection	38/224	17.0%	(12.3-22.5%)
Meningitis	0/44	0.0%	(0.0-8.0%)
Any invasive bacterial infection	38/224	17.0%	(12.3-22.5%)	38/225	16.9%	(12.2-22.4%)
Arbovirus
Chikungunya	17/176	9.7%	(5.7-15.0%)
Dengue	14/180	7.8%	(4.3-12.7%)
Any arbovirus infection	31/182	17.0%	(11.9-23.3%)	31/225	13.8%	(9.6-19.0%)
Malaria
Falciparum malaria	21/219	9.6%	(6.0-14.3%)	21/225	9.3%	(5.9-13.9%)
Invasive fungal infection
Bloodstream infection	3/224	1.3%	(0.3-3.9%)
Meningitis	4/44	9.1%	(2.5-21.7%)
Any invasive fungal infection	5/224	2.2%	(0.7-5.1%)	5/225	2.2%	(0.7-5.1%)
Rickettsioses
Spotted fever group	4/147	2.7%	(0.7-6.8%)
Epidemic typhus group	0/147	0.0%	(0.0-2.5%)
Total	4/147	2.7%	(0.7-6.8%)	4/225	1.8%	(0.5-4.5%)
Other
Leptospirosis	2/179	1.1%	(0.1-4.0%)	2/225	0.9%	(0.1-3.2%)
Borreliosis	1/122	0.8%	(0.0-4.5%)	1/225	0.4%	(0.0-2.5%)
Rift Valley fever	1/122	0.8%	(0.0-4.5%)	1/225	0.4%	(0.0-2.5%)


# Table of diagnoses stratified by HIV status

BTaetiology %>%
  select(hivstatus, tb, arbovirus,
         inv.bacterial, inv.fungal, malaria) %>% 
  filter(hivstatus != "Unknown") -> t

t[is.na(t)] <- 0

t %>%  
  pivot_longer(-hivstatus) %>% 
  group_by(name) %>% 
  summarise(n.1.reactive = sum(value == 1 & hivstatus == "Reactive"),
            n.tot.reactive = sum(hivstatus == "Reactive"),
            n.1.nonreactive = sum(value == 1 & hivstatus == "Non reactive"),
            n.tot.nonreactive = sum(hivstatus == "Non reactive"),
            bdiff = list(boot.two.per(value, hivstatus, propz))
            ) %>% 
  mutate(diff = map_dbl(bdiff, "Observed"),
         diff.lci = map_dbl(bdiff, 
                            function(x) 
                              unlist((x["Confidence.limits"][[1]][[1]]))),
          diff.uci = map_dbl(bdiff, 
                             function(x) 
                               unlist((x["Confidence.limits"][[1]][[2]]))),
         react.str = paste0(n.1.reactive, "/", n.tot.reactive,
                           " (", sp_dc(100*n.1.reactive/ n.tot.reactive,0), 
                           "%)"),
         nonreact.str = paste0(n.1.nonreactive, "/", n.tot.nonreactive,
                           " (", sp_dc(100*n.1.nonreactive/n.tot.nonreactive,0), 
                           "%)"),
         diffstr = paste0(sp_dc(diff*100,0), "% (95% CI ",
                          sp_dc(diff.lci*100,0), "-", 
                          sp_dc(diff.uci*100,0),")")
         ) %>% 
  select(name, react.str, nonreact.str, diffstr) -> tout

tout %>% 
  mutate(name = recode(name,
                       tb = "Tuberculosis",
                       inv.fungal = "Invasive fungal infection",
                       arbovirus = "Arboviral infection",
                       inv.bacterial = "Invasive bacterial infection",
                       malaria = "Malaria")) %>% 
kbl( col.names = c("Diagnosis", "HIV+", "HIV-", "Difference"),
 caption = "TABLE 4: Diagnosis stratified by HIV status") %>%
kable_classic(full_width = FALSE)

TABLE 4: Diagnosis stratified by HIV status
Diagnosis	HIV+	HIV-	Difference
Arboviral infection	8/143 (6%)	19/70 (27%)	22% (95% CI 11-33)
Invasive bacterial infection	21/143 (15%)	14/70 (20%)	5% (95% CI -6-17)
Invasive fungal infection	5/143 (3%)	0/70 (0%)	-3% (95% CI -7–1)
Malaria	6/143 (4%)	12/70 (17%)	13% (95% CI 4-23)
Tuberculosis	71/143 (50%)	1/70 (1%)	-48% (95% CI -57–39)


# Table of acute and convalescent serology results

BTsera %>% 
  select(contains("pid") | starts_with("day_") & 
           !ends_with("OD") & !ends_with("dilution")) %>%
  pivot_longer(-pid, 
               names_to = c("day", "org", "Ig"), 
               names_pattern = "day_(.*?)_(.*?)_(...)" ) %>%
  mutate(day = paste0("day ", day)) %>% 
  group_by(day,org,Ig) %>% 
  summarise(n_pos = sum(value == "pos", na.rm = TRUE),
            n_neg = sum(value == "neg" | value == "ind", na.rm = TRUE),
            n_prev = n_pos/(n_pos+n_neg),
            n_prev_lci =binom.test(n_pos, n_pos+n_neg)$conf.int[1],
            n_prev_uci =binom.test(n_pos, n_pos+n_neg)$conf.int[2],
            prev_str = paste0(n_pos,"/", n_pos + n_neg,
                                " (",sp_dc(n_prev*100,1), "% [",
                                sp_dc(n_prev_lci*100,1), "-",
                                sp_dc(n_prev_uci*100,1),"])"
                                )
  ) %>% ungroup() %>%
  select(day, org, Ig, prev_str) %>%
  pivot_wider(names_from = c("day","Ig"), 
              values_from = "prev_str", 
              values_fill = "&#8722" ) %>%
  mutate(org = case_when(
    org == "chik" ~ "Chikungunya",
    org == "den" ~ "Dengue",
    org == "lepto" ~ "Leptospirosis",
    org == "ET" ~ "Epidemic typhus group",
    org == "SF" ~ "Spotted fever group"
  )) %>%
  kbl(
    caption = "SUPPLEMENTARY TABLE 4: Acute and convalescent serology results", 
    col.names = c("Organism", "IgG", "IgM", "IgG","IgM"),
    escape = FALSE) %>%
  kable_classic(full_width = FALSE) %>%
  add_header_above(c(" ", "Day 0" = 2, "Day 28" = 2))
#> `summarise()` has grouped output by 'day', 'org'. You can override using the `.groups` argument.

SUPPLEMENTARY TABLE 4: Acute and convalescent serology results
	Day 0		Day 28
Organism	IgG	IgM	IgG	IgM
Chikungunya	16/41 (39.0% [24.2-55.5])	9/41 (22.0% [10.6-37.6])	51/146 (34.9% [27.2-43.3])	15/143 (10.5% [6.0-16.7])
Dengue	1/36 (2.8% [0.1-14.5])	5/36 (13.9% [4.7-29.5])	12/144 (8.3% [4.4-14.1])	13/147 (8.8% [4.8-14.6])
Leptospirosis	−	0/35 (0.0% [0.0-10.0])	−	2/146 (1.4% [0.2-4.9])
Epidemic typhus group	−	−	3/147 (2.0% [0.4-5.8])	0/147 (0.0% [0.0-2.5])
Spotted fever group	−	−	61/147 (41.5% [33.4-49.9])	2/147 (1.4% [0.2-4.8])


BTsera %>% 
  select(matches("pid|dilution")) %>% 
  pivot_longer(-pid) %>% 
  filter(!is.na(value)) %>% 
  mutate(value = if_else(value == "neg", "Negative", value),
         value = factor(value, 
                        levels = c("Negative",
                                   "1:64",
                                   "1:128",
                                   "1:256",
                                   "1:512",
                                   "1:1024")),
         Pathogen = if_else(grepl("SF", name),
                                  "Spotted fever",
                                  "Epidemic typhus"),
         Antibody = str_extract(name, "IgG|IgM")) %>% 
  ggplot(aes(value)) +
  geom_bar(position = "dodge") +
  facet_grid(Pathogen ~ Antibody) +
  theme_bw() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) + 
  labs(x = "Antibody titre", y = "n") -> ricks_titre_plot

if (write_figs) {
  ggsave(
   here("figures/SUP_F3_ricks-titres.pdf"),
   ricks_titre_plot, width = 4, height = 3.2)
  
 
  ggsave(
   here("figures/SUP_F3_ricks-titres.eps"),
   ricks_titre_plot, width = 4, height = 3.2,
   dpi = 600)
}


# Table of differences in variables  between tb or not tb
# expressed as bootstrapped difference in expressed as median or proportion

BTparticipants %>% 
  select(-c(hivcpt,ever_tb, tbongoing, art_time,hivonart,
            d28_death, d90_death, d180_death, t, died, urea)) %>% 
  left_join(select(BTaetiology, pid, tb )) %>% 
  mutate(hivstatus = case_when(hivstatus == "Non reactive" ~ "0",
                               hivstatus == "Reactive" ~ "1",
                               TRUE ~ NA_character_),
         ptsex = if_else(ptsex == "Male", "1", "0"),
         tb = as.character(if_else(is.na(tb), 0,tb))) -> 
  df.sum.tbl
#> Joining, by = "pid"

# bootstrap differences in proportions for character vars


left_join(
  df.sum.tbl %>%
    select(where(is.character)) %>%
    select(-pid) %>%
    pivot_longer(-tb) %>%
    dplyr::group_by(name, tb) %>%
    summarise(str = paste0(
      sum(value == 1, na.rm = TRUE),
      " (",
      sp_dc(100 * sum(value == 1, na.rm = TRUE) /
              sum(!is.na(value)), 0),
      "%)"
    )) %>%
    pivot_wider(
      id_cols = "name",
      names_from = "tb",
      values_from = "str"
    ),
  
  df.sum.tbl %>%
    select(where(is.character)) %>%
    select(-pid) %>%
    pivot_longer(-tb) %>%
    dplyr::group_by(name) %>%
    dplyr::summarise(bs = list(boot.two.bca(value,
                                            tb,
                                            propz))) %>%
    mutate(
      diff = map_dbl(bs, "Observed"),
      diff.lci = map_dbl(bs,
                         function(x)
                           unlist((x["Confidence.limits"][[1]][[1]]))),
      diff.uci = map_dbl(bs,
                         function(x)
                           unlist((x["Confidence.limits"][[1]][[2]])))
    ) %>%
    select(-bs) %>%
    mutate(diff.str = paste0(
      sp_dc(diff * 100, 0),
      "% (",
      sp_dc(diff.lci * 100, 0),
      " to ",
      sp_dc(diff.uci * 100, 0),
      "%)"
    )) ,
  by = "name"
) ->  diff.in.prop.cat.vars
#> `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.

# bootstrap differences in medians for numeric vars

df.sum.tbl %>% 
  select(where(is.numeric) | ends_with("tb")) %>% 
  pivot_longer(-tb) %>%
  filter(!is.na(tb) & !is.na(value)) %>% 
  dplyr::group_by(name) %>%
  dplyr::summarise(bs = list(boot.two.per(value, tb, median))) %>%
  mutate(diff = map_dbl(bs, "Observed"),
         diff.lci = map_dbl(bs, 
                            function(x) 
                              unlist((x["Confidence.limits"][[1]][[1]]))),
          diff.uci = map_dbl(bs, 
                             function(x) 
                               unlist((x["Confidence.limits"][[1]][[2]])))) %>% 
  select(-bs) %>% 
  mutate(diff.str = paste0(sp_dc(diff,1),
                           " (", 
                           sp_dc(diff.lci,1),
                           " to ",
                           sp_dc(diff.uci,1),
                           ")"
                           )
  ) -> diff.in.median.num.vars


#summary stats for tb and not tb groups

df.sum.tbl %>% 
  select(where(is.numeric) | ends_with("tb")) %>%
  pivot_longer(-tb) %>% filter(!is.na(tb)) %>%
  dplyr::group_by(name, tb) %>% 
  dplyr::summarise( median = median(value, na.rm = T),
                    LQ = quantile(value, 0.25, na.rm = T),
                    UQ = quantile(value, 0.75, na.rm = T)) %>% 
  mutate(
    med_str = paste0(
      sp_dc(median, 1), " (",
      sp_dc(LQ, 1), "-",
      sp_dc(UQ, 1) ,")")
  ) %>% 
  select(name, tb, med_str) %>% 
  pivot_wider(names_from = tb, values_from = med_str) -> 
  summ.stats.by.group
#> `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.


st4 <- merge(summ.stats.by.group, diff.in.median.num.vars, all.x = T)

st4 <- bind_rows(st4,diff.in.prop.cat.vars)


st4$boldcol <- FALSE 
st4$boldcol[(st4$diff.lci >= 0 &
                st4$diff.uci > 0) | (st4$diff.lci < 0 & 
                                        st4$diff.uci <= 0)] <- TRUE



st4[match(c("calc_age", "ptsex", "days_unwell", 
             "hivstatus", "cd4_absolute", 
             "screentemp", "t0hr", "t0sbp", "t0dbp", 
             "t0rr", "t0spo2","gcs", "ustand", 
             "haemoglobin", "wcc", "platelets", "sodium", 
             "co2", "creatinine", 
             "lactate"), st4$name),] -> st4



st4 <- st4[c("name", "1", "0", "diff.str", "boldcol")]
names(st4) <- c("Variable", "TB", "no TB", "Difference", "boldcol")

boldrows <- which(st4$boldcol %in% TRUE)

st4$Variable[st4$Variable == "calc_age"] <- "Age (years)"
st4$Variable[st4$Variable == "ptsex"] <- "Male sex"
st4$Variable[st4$Variable == "hivstatus"] <- "HIV Infected"
st4$Variable[st4$Variable == "haemoglobin"] <- "Haemoglobin (x10^9^} g/dL)"
st4$Variable[st4$Variable == "cd4_absolute"] <- "CD4 count 10^6^ /L"
st4$Variable[st4$Variable == "screentemp"] <- "Temperature (C)"
st4$Variable[st4$Variable == "t0hr"] <- "Heart rate (beats/min)"
st4$Variable[st4$Variable == "t0sbp"] <- "Systolic BP (mmHg)"
st4$Variable[st4$Variable == "t0dbp"] <- "Diastolic BP (mmHg)"
st4$Variable[st4$Variable == "t0rr"] <- "Respiratory rate (breaths/min)"
st4$Variable[st4$Variable == "t0spo2"] <- "Oxygen saturation (%)"
st4$Variable[st4$Variable == "gcs"] <- "GCS"
st4$Variable[st4$Variable == "ustand"] <- "Unable to stand"
st4$Variable[st4$Variable == "lactate"] <- "Lactate (mmol/L)"
st4$Variable[st4$Variable == "wcc"] <- "White cell count (x10^9^)"
st4$Variable[st4$Variable == "platelets"] <- "Platelet count (x10^9^ /L)"
st4$Variable[st4$Variable == "sodium"] <- "Sodium (mmol /L)"
st4$Variable[st4$Variable == "co2"] <- "Bicarbonate (mmol /L)"
st4$Variable[st4$Variable == "urea"] <- "Urea (mmol /L)"
st4$Variable[st4$Variable == "creatinine"] <- "Creatinine (mmol /L)"
st4$Variable[st4$Variable == "days_unwell"] <- "Length of time unwell for (days)"



kbl(select(st4,-boldcol), 
      row.names = F, caption = "SUPPLEMENTARY TABLE 5: Univariable associations with TB diagnosis. Numeric variables are presented as median (IQR) and categorical variables as proportions. Difference column shows difference in medians or difference in proportions with 95% confidence intervals. Variables shown in bold are those for which the 95% confidence intervals do not cross 0." ) %>%
 kable_classic(full_width = FALSE) %>%
  row_spec(boldrows, bold = T)

SUPPLEMENTARY TABLE 5: Univariable associations with TB diagnosis. Numeric variables are presented as median (IQR) and categorical variables as proportions. Difference column shows difference in medians or difference in proportions with 95% confidence intervals. Variables shown in bold are those for which the 95% confidence intervals do not cross 0.
Variable	TB	no TB	Difference
Age (years)	37.2 (31.1-42.7)	35.3 (26.3-44.2)	-1.9 (-5.0 to 2.6)
Male sex	37 (49%)	77 (52%)	3% (-11 to 17%)
Length of time unwell for (days)	14.0 (6.0-28.5)	7.0 (3.0-14.0)	-7.0 (-9.0 to 0.0)
HIV Infected	71 (99%)	72 (51%)	-45% (-54 to -35%)
CD4 count 10⁶ /L	100.0 (35.5-209.5)	215.0 (67.5-334.0)	115.0 (59.0 to 158.0)
Temperature (C)	38.5 (38.0-39.0)	38.5 (37.9-39.0)	0.0 (-0.2 to 0.3)
Heart rate (beats/min)	125.0 (117.8-136.2)	116.0 (98.0-131.0)	-9.0 (-17.0 to -4.5)
Systolic BP (mmHg)	93.0 (85.0-111.5)	99.0 (87.0-120.0)	6.0 (-4.5 to 12.0)
Diastolic BP (mmHg)	67.0 (52.8-72.0)	66.0 (57.0-77.0)	-1.0 (-4.5 to 7.5)
Respiratory rate (breaths/min)	34.0 (32.0-38.0)	34.0 (32.0-36.0)	0.0 (-2.0 to 2.0)
Oxygen saturation (%)	97.0 (94.0-98.0)	96.0 (95.0-98.0)	-1.0 (-1.0 to 0.5)
GCS	15.0 (15.0-15.0)	15.0 (15.0-15.0)	0.0 (0.0 to 0.0)
Unable to stand	0.0 (0.0-1.0)	0.0 (0.0-1.0)	0.0 (0.0 to 0.0)
Haemoglobin (x10⁹} g/dL)	8.9 (6.6-11.0)	11.5 (9.5-13.9)	2.6 (1.3 to 3.7)
White cell count (x10⁹)	6.3 (4.2-8.9)	6.8 (4.5-11.7)	0.5 (-0.8 to 2.2)
Platelet count (x10⁹ /L)	216.0 (146.5-299.0)	218.5 (145.8-295.5)	2.5 (-51.0 to 50.5)
Sodium (mmol /L)	132.0 (127.0-135.5)	134.0 (132.0-137.0)	2.0 (1.0 to 6.0)
Bicarbonate (mmol /L)	18.0 (14.5-20.0)	20.0 (17.0-23.0)	2.0 (0.0 to 3.0)
Creatinine (mmol /L)	74.5 (55.8-100.5)	78.0 (60.0-103.0)	3.5 (-6.0 to 17.0)
Lactate (mmol/L)	3.2 (2.3-5.2)	3.4 (2.2-5.1)	0.2 (-0.7 to 0.8)


# Table of positive tests stratified by HIV status

BTaetiology %>% 
  mutate(total = 1) %>%
  select(-c(arbovirus, ricks,
             tb, inv.bacterial, inv.fungal)) %>%  
  pivot_longer(-c(hivstatus, pid)) %>% 
  group_by(hivstatus, name) %>%
  summarise(pos = as.integer(sum(value,na.rm = TRUE)),
            tests = sum(!is.na(value)),
            prop = pos/tests) %>%
  ungroup() %>%
  filter(tests> 0) %>%
  rowwise() %>%
  mutate(
    str = case_when(
      name == "total" ~ as.character(tests),
      tests == 0 ~ "-",
      TRUE ~ paste0(pos,"/", tests, " (",
        sp_dc(100*pos/tests,0), "% [", 
        sp_dc(100*binom.test(x=pos,n=tests)$conf.int[1],0), "-",
        sp_dc(100*binom.test(x=pos,n=tests)$conf.int[2],0),"])")
      )
    ) %>% 
  ungroup() %>%
  select(-c(pos,tests, prop)) %>%
  pivot_wider(names_from = hivstatus, 
              values_from = str, 
              values_fill = "&#8722") %>%
  arrange(fct_relevel(name, 
    "total", 
    "uLAM","Xpert", "mtb bsi",
    "bsi.bacterial","csf.bacterial","bact_target_pcr",
    "chik", "dengue",
    "malaria",
    "bsi.fungal","csf.fungal","CrAg_LFA",
    "SF", "ET", 
    "lepto", "borrelia", "rift_valley_fever")) %>%
  mutate(name = case_when(
    name == "bact_target_pcr" ~ "PCR: Bacterial pathogen DNA detected",
    name == "bsi.bacterial" ~ "Aerobic blood culture",
    name == "csf.bacterial" ~ "CSF culture",
    name == "bsi.fungal" ~ "Aerobic blood culture",
    name == "csf.fungal" ~ "CSF culture",
    name == "CrAg_LFA" ~ "CSF CrAg",
    name == "borrelia" ~ "PCR: Borrelia DNA detected",
    name == "chik" ~ "Chikununya IgM",
    name == "dengue" ~ "Dengue IgM",
    name == "ET" ~ "Epidemic typhus IgG ≥ 1:512",
    name == "lepto" ~ "Leptospirosis IgM",
    name == "malaria" ~ "P. falciparum RDT",
    name == "rift_valley_fever" ~ "PCR: Rift valley fever virus detected",
    name == "SF" ~ "Spotted fever IgG ≥ 1:512",
    name == "total" ~ "Number of participants",
    name == "Xpert" ~ "Sputum Xpert",
    name == "uLAM" ~ "Urinary LAM lateral flow assay",
    name == "mtb bsi" ~ "Mycobacterial blood culture"
  )) %>%
  kbl(caption = 
      "SUPPLEMENTARY TABLE 6: Positive test reaults stratified by HIV status",
      col.names = c("Test", 
                    "Non reactive",
                    "Reactive",
                    "Unknown"),
      row.names = FALSE,
      escape = FALSE) %>%
  kable_classic(full_width = FALSE) %>%
  add_header_above(c(" " = 1, "HIV Status" = 3)) %>%
  pack_rows("TB diagnostics", 2,4) %>%
  pack_rows("Bacterial diagnostics", 5,7) %>%
  pack_rows("Arboviral diagnostics", 8,9) %>%
  pack_rows("Malaria diagnostics", 10,10) %>%
  pack_rows("Fungal diagnostics", 11,13) %>%
  pack_rows("Rickettsial diagnostics",14,15 ) %>% 
  pack_rows("Leptospirosis diagnostics",16,16 ) %>% 
  pack_rows("PCR Array card",17,18) %>%
  footnote(general = 
             c(str_wrap(paste0("Urinary LAM testing and mycobacterial ",
             "blood culture testing were only carried out in HIV ", 
             "infected or HIV unknown participants."), width = 80),
             "TB = Tuberculosis", "LAM = Lipoaribomannan",
             "CSF = Cerebrspinal fluid", "CrAg = Cryptoccal antigen"))
#> `summarise()` has grouped output by 'hivstatus'. You can override using the `.groups` argument.

SUPPLEMENTARY TABLE 6: Positive test reaults stratified by HIV status
	HIV Status
Test	Non reactive	Reactive	Unknown
Number of participants	70	143	12
TB diagnostics
Urinary LAM lateral flow assay	−	70/136 (51% [43-60])	4/9 (44% [14-79])
Sputum Xpert	1/8 (12% [0-53])	7/35 (20% [8-37])	0/1 (0% [0-98])
Mycobacterial blood culture	−	7/128 (5% [2-11])	1/10 (10% [0-45])
Bacterial diagnostics
Aerobic blood culture	14/70 (20% [11-31])	21/142 (15% [9-22])	3/12 (25% [5-57])
CSF culture	0/12 (0% [0-26])	0/31 (0% [0-11])	0/1 (0% [0-98])
PCR: Bacterial pathogen DNA detected	5/49 (10% [3-22])	11/66 (17% [9-28])	1/7 (14% [0-58])
Arboviral diagnostics
Chikununya IgM	9/47 (19% [9-33])	5/120 (4% [1-9])	3/9 (33% [7-70])
Dengue IgM	10/50 (20% [10-34])	3/121 (2% [1-7])	1/9 (11% [0-48])
Malaria diagnostics
P. falciparum RDT	12/69 (17% [9-28])	6/138 (4% [2-9])	3/12 (25% [5-57])
Fungal diagnostics
Aerobic blood culture	0/70 (0% [0-5])	3/142 (2% [0-6])	0/12 (0% [0-26])
CSF culture	0/12 (0% [0-26])	4/31 (13% [4-30])	0/1 (0% [0-98])
CSF CrAg	0/6 (0% [0-46])	4/23 (17% [5-39])	−
Rickettsial diagnostics
Spotted fever IgG ≥ 1:512	2/45 (4% [1-15])	2/94 (2% [0-7])	0/8 (0% [0-37])
Epidemic typhus IgG ≥ 1:512	0/45 (0% [0-8])	0/94 (0% [0-4])	0/8 (0% [0-37])
Leptospirosis diagnostics
Leptospirosis IgM	1/50 (2% [0-11])	1/120 (1% [0-5])	0/9 (0% [0-34])
PCR Array card
PCR: Borrelia DNA detected	0/49 (0% [0-7])	1/66 (2% [0-8])	0/7 (0% [0-41])
PCR: Rift valley fever virus detected	1/49 (2% [0-11])	0/66 (0% [0-5])	0/7 (0% [0-41])
Note:
Urinary LAM testing and mycobacterial blood culture testing were only carried out in HIV infected or HIV unknown participants.
TB = Tuberculosis
LAM = Lipoaribomannan
CSF = Cerebrspinal fluid
CrAg = Cryptoccal antigen


# UpSet plot of diagnoses 

left_join(
BTaetiology %>% 
  select(pid, hivstatus, tb, inv.bacterial, 
         arbovirus, malaria, inv.fungal, 
         ricks, lepto,
         borrelia, rift_valley_fever) %>% 
  rename("Tuberculosis" = tb,
        "Invasive bacterial infection" = inv.bacterial, 
        Arbovirus = arbovirus,
        Malaria = malaria,
        "Invasive fungal infection" = inv.fungal,
        "Rickettsioses" = ricks, 
        "Leptospirosis" = lepto) %>% 
  as.data.frame,
  BTtreatment %>% 
    mutate(abz = !is.na(timeto_ab)) %>% 
    select(pid, abz)
) -> d
#> Joining, by = "pid"

d[-c(1:2)][is.na(d[-c(1,2)])] <- 0

upset(as.data.frame(d), nsets = 5,order = "freq", text.scale = 1.5,
      queries = list(
          list(query = elements, params = list("abz", "TRUE"), 
               color = viridis_pal( option = "C")(6)[4],
               active = T, 
               query.name = "abx"))) -> upsetplot

upsetplot

FIGURE 1: UpSet plot showing overlapping diagnoses. Only participants who had a diagnosis are included in this plot, and the five most frequent diagnoses shown, demonstrating that most participants had only one diagnosis. Red colour of bar indicates receipt of antibacterial therapy, showing that almost all participants received antibacterial therapy despite no demonstrated invasive bacterial infection in many cases.

# to save - followed by a hacky legend to add



if (write_figs) {
  tiff(
    file = here("figures/upset_fig_files/MAIN_upset_diagnosis.eps"),
    width = 8,
    height = 5.5,
    units = "in",
    res = 600
  )
  upsetplot
  dev.off()
  
  pdf(
    file = here("figures/upset_fig_files/MAIN_upset_diagnosis.pdf"),
    width = 8,
    height = 5.5,
    onefile = FALSE
  )
  upsetplot
  dev.off()
  
  
  mtcars %>%
    ggplot(aes(cyl, fill = disp > 160)) +
    geom_bar() +
    scale_fill_manual(
      values = c("gray23", viridis_pal(option = "C")(6)[4]),
      labels = c("No antibacterials",
                 "Received antibacterials")
    ) +
    theme(legend.title = element_blank()) -> skank
  
  ggsave(here("figures/upset_fig_files/hacky_legend_for_upset.tiff"),
         skank, dpi = 600)
}


# Plot pathogens identified in aerobic blood culture and on array card 

BTbc %>% select(c(typhi, saen, saty, esco,
                     klpn,hib, stpn, crne,acba,enfam, enfas,stau,prot)) %>% 
  pivot_longer(everything()) %>% 
  filter(value != 0) %>% 
  mutate(name = case_when(
    name == "typhi" ~ "Salmonella Typhi",
    name == "saen" ~ "Salmonella Enteritides",
    name == "esco" ~ "Escherichia coli",
    name == "saty" ~ "Salmonella Typhimurium",
    name == "crne" ~ "Cryptococcus neoformans",
    name == "stau" ~ "Staphylococcus aureus",
    name == "klpn" ~ "Klebsiella pneumoniae",
    name == "hib" ~ "Haemophilus influenzae",
    name == "stpn" ~ "Streptococcus pnemoniae",
    name == "acba" ~ "Acinetobacter baumannii",
    name == "enfam" ~ "Enterococcus faecium",
    name == "prot" ~ "Proteus mirabilis",
  ),
  name = fct_rev(fct_infreq(name))) %>% 
  ggplot(aes(name)) + geom_bar() + coord_flip() + theme_bw() +
  labs(x = "", y = "n")  +
  scale_y_continuous(breaks = c(0:10)) -> p1

BTarraycard %>% 
  select(contains("array_")) %>%
  pivot_longer(everything()) %>%
  mutate(name = str_replace_all(name, "array_card_", ""),
         name = str_to_title(name),
         name = str_replace_all(name, "\\.", " ")) %>% 
  filter(value == TRUE) %>% 
  mutate(name = case_when(
           name == "Ebv" ~ "Epstein-Barr virus",
           name == "Pan strep" ~ "Streptococcus spp.",
           name == "Strep pneumo" ~ "Streptococcus pneumoniae",
           name == "Cmv" ~ "Cytomegalovirus",
           name == "Dengue" ~ "Dengue virus",
           name == "Enterobacteria" ~ "Enterobacteria spp.",
           name == "Pan borrelia" ~ "Borrelia spp.",
           name == "Rvf" ~ "Rift Valley fever virus",
           name == "Salmonella hila" ~ "Salmonella spp.",
           TRUE ~ name
           )) %>% 
   mutate(name = fct_rev(fct_infreq(name))) %>% 
  ggplot(aes(name)) + geom_bar() + coord_flip() + theme_bw() +
  labs(x = "", y = "n")  +
  scale_y_continuous(breaks = c(0:10)) -> p2

(p1 | p2) + plot_annotation(tag_levels = 'A') -> p
p

SUPPLEMENTARY FIGURE 4: Pathogens identified by (A) aerobic blood culture and (B) multiplex PCR on blood. 26 blood cultures in 24/224 participants grew 28 pathogens in total. 26 pathogen PCR targets were identified in 31/122 samples. n here is number of samples.

if (write_figs) {
  
ggsave(here("figures/SUP_F4_BC_PCR_dx.eps"), width = 8, height = 3, units = "in", dpi = 600)
ggsave(here("figures/SUP_F4_BC_PCR_dx.pdf"), width = 8, height = 3, units = "in", dpi = 600)
}


# Plot bacterial pathogens identified

BTbc %>% 
  select(c(pid,typhi, saen, saty, esco,
           klpn,hib, stpn, crne,
           acba,enfam, enfas,stau,prot)) %>% 
  left_join(select(BTparticipants ,pid, hivstatus), by = "pid") %>% 
  mutate(hivstatus = if_else(is.na(hivstatus), "Unknown", hivstatus)) %>% 
  left_join(
    BTarraycard %>% 
      select(contains("array_") | contains("pid")) %>% 
      pivot_longer(-pid) %>% 
      mutate(value = if_else(is.na(value), 0, as.numeric(value))) %>% 
      pivot_wider(id_cols = pid, 
                  names_from = name, 
                  values_from = value),
    by = c("pid" = "pid")
  ) %>% 
  pivot_longer(-c(pid, hivstatus)) %>% 
  filter(value != 0) %>% 
  mutate(name = case_when(
    name == "typhi" ~ "Salmonella Typhi",
    name == "saen" ~ "Salmonella Enteritides",
    name == "esco" ~ "Escherichia coli",
    name == "saty" ~ "Salmonella Typhimurium",
    name == "crne" ~ "Cryptococcus neoformans",
    name == "stau" ~ "Staphylococcus aureus",
    name == "klpn" ~ "Klebsiella pneumoniae",
    name == "hib" ~ "Haemophilus influenzae",
    name == "stpn" ~ "Streptococcus pneumoniae",
    name == "acba" ~ "Acinetobacter baumannii",
    name == "enfam" ~ "Enterococcus faecium",
    name == "prot" ~ "Proteus mirabilis",
    TRUE ~ name
  )) %>% 
  mutate(name = str_replace_all(name, "array_card_", ""),
         name = str_to_title(name),
         name = str_replace_all(name, "\\.", " ")) %>% 
  filter(value == TRUE) %>% 
  mutate(name = case_when(
           name == "Ebv" ~ "Epstein-Barr virus",
           name == "Pan strep" ~ "Streptococcus spp.",
           name == "Strep pneumo" ~ "Streptococcus pneumoniae",
           name == "Cmv" ~ "Cytomegalovirus",
           name == "Dengue" ~ "Dengue virus",
           name == "Enterobacteria" ~ "Enterobacteria spp.",
           name == "Pan borrelia" ~ "Borrelia spp.",
           name == "Rvf" ~ "Rift Valley fever virus",
           name == "Salmonella hila" ~ "Salmonella spp.",
           TRUE ~ name)) %>%
  mutate(name = str_to_title(name)) %>% 
  mutate(group = case_when(
    str_detect(name, "Escherichia") ~ "Enterobacterales",
    str_detect(name, "Enterobact") ~ "Enterobacterales",
    str_detect(name, "Klebsiell") ~ "Enterobacterales",
    str_detect(name, "Proteus") ~ "Enterobacterales",
    str_detect(name, "Salmone") ~ "Salmonellae spp.",
    str_detect(name, "Streptococc") ~ "Streptococci spp.",
    TRUE ~ "Other"
   )) %>% 
  filter(!str_detect(name, "[vV]irus") &
           !str_detect(name, "Cryptoco")) %>% 
  mutate(group = str_replace(group, "\\s([A-Z])", tolower)) %>% 
  mutate(name = str_replace(name, "\\s([A-Z])", tolower)) %>% 
  mutate(name = str_replace(name, "typhi", "Typhi"),
         name = str_replace(name, "typhimurium", "Typhimurium")) %>% 
  unique -> bacteria.by.participant

bacteria.by.participant %>% 
  mutate(name = fct_rev(fct_infreq(name))) %>% 
  ggplot(aes(name, fill = hivstatus)) +
  geom_bar() +
  coord_flip() +
  theme_bw() +
  labs(x = "", y = "n") +
  scale_y_continuous(breaks = c(0:20)) +
  labs(fill = "HIV status") +
  scale_fill_manual(values = viridis_pal()(6)[c(2,4,5)]) -> pb.2#+
 # scale_fill_viridis_d(option = "D")

bacteria.by.participant %>% 
  mutate(group = if_else(group == "Enterobacterales",
                         "non-Salmonella\nEnterobacterales",
                         group)) %>%  
  mutate(group = fct_rev(fct_infreq(group))) %>% 
  ggplot(aes(group, fill = hivstatus)) +
  geom_bar() +
  coord_flip() +
  theme_bw() +
  labs(x = "", y = "n") +
  scale_y_continuous(breaks = c(0:20)) +
  labs(fill = "HIV status") +
  scale_fill_manual(values = viridis_pal()(6)[c(2,4,5)]) -> pb.1

(pb.1 | pb.2) + 
  plot_layout(guides = 'collect') + 
  plot_annotation(tag_levels = "A") -> pb

pb

SUPPLEMENTARY FIGURE 5: number of participants in whom a bacterial pathogen was identified: aggregated (A) and to species level (B). 43 bacterial pathogens were identified in 38 participants. n here is number of participants.


if (write_figs) {
  
ggsave(here("figures/SUP_F5_bact_pathogens.tiff"), pb, height = 4, width = 10, units = "in",dpi = 600)
ggsave(here("figures/SUP_F5_bact_pathogens.pdf"), pb, height = 4, width = 10, units = "in")
}


# Euler diagram of overlapping diagnoses
      
d$`No diagnosis` = as.numeric(apply(d[-c(1,2, ncol(d))], 1, sum) == 0)
d$Other <- as.numeric(d$Leptospirosis + d$Rickettsioses + 
  d$borrelia + d$rift_valley_fever > 0) 

as.ggplot(
    plot(
      euler(
        d %>% 
          select(-c(pid, hivstatus, borrelia, Leptospirosis, Rickettsioses,
                    borrelia, rift_valley_fever, Other, abz)) %>% 
          rename("Bacterial" = "Invasive bacterial infection",
                 "Fungal" = "Invasive fungal infection") %>% 
          as.data.frame(), 
        shape = "ellipse" 
      ),
      fills = list(fill = viridis_pal()(6), alpha = 0.7)
    )
) -> p1


p1

SUPPLEMENTARY FIGURE 6: Euler diagrm shwoing overlapping diagnoses

if (write_figs) {
  
ggsave(here("figures/SUP_F6_euler_dx.tiff"), p1, width = 6, height = 4, units = "in", dpi = 600)
ggsave(here("figures/SUP_F6_euler_dx.pdf"), p1, width = 6, height = 4, units = "in")
}

Outcome


# Table of bivariable comparison: survived vs died at 30 days
# Prepare data frame

BTparticipants %>% 
  select(-c(d90_death, d180_death, t, died, art_time,
            hivcpt, ever_tb, tbongoing)) %>% 
  left_join(BTtreatment %>% 
              transmute(pid = pid,
                     tb.rx = !is.na(timeto_antitb),
                     fung.rx = !is.na(timeto_antifungal),
                     mal.rx = !is.na(timeto_antimalarial),
                     time_to_abx = timeto_ab,
                     fluid.6hr = iv_fluid_6hr)
  ) %>%
  left_join(select(BTaetiology, 
                   pid, 
                   malaria, 
                   dengue, 
                   chik,
                   arbovirus,
                   inv.bacterial, 
                   inv.fungal, 
                   tb) %>% 
              mutate(across(!contains("pid"), ~ if_else(is.na(.x), 0, .x)))
                   ) %>% 
  mutate(ustand = as.character(ustand),
         malaria = as.character(malaria),
         dengue = as.character(dengue),
         chik = as.character(chik),
         inv.bacterial = as.character(inv.bacterial),
         inv.fungal = as.character(inv.fungal),
         tb = as.character(tb),
         tb.rx = as.character(as.numeric(tb.rx)),
         fung.rx = as.character(as.numeric(fung.rx)),
         mal.rx = as.character(as.numeric(mal.rx)),
         time_to_abx = as.numeric(time_to_abx),
         abx = as.character(as.numeric(!is.na(time_to_abx))),
         fluid.6hr = fluid.6hr /1000,
         d28_death = as.character(d28_death),
         ptsex = recode(ptsex,
                        "Male" = "1",
                        "Female" = "0"),
         hivstatus = recode(hivstatus,
                        "Reactive" = "1",
                        "Non reactive" = "0",
                        .default = NA_character_),
         ) %>% 
  mutate(no_diagnosis = 1,
         no_diagnosis = case_when(
         malaria == 1 ~ 0,
         dengue == 1 ~ 0,
         chik == 1 ~ 0,
         inv.bacterial == 1 ~ 0,
         inv.fungal ==1 ~ 0,
         tb == 1 ~ 0,
         TRUE ~ no_diagnosis),
         no_diagnosis = as.character(no_diagnosis),
         tb = if_else(is.na(tb), "0", tb),
         malaria = if_else(is.na(malaria), "0", malaria),
         dengue = if_else(is.na(dengue), "0", dengue),
         chik = if_else(is.na(chik), "0", chik),
         inv.bacterial = if_else(is.na(inv.bacterial), "0", inv.bacterial),
         inv.fungal = if_else(is.na(inv.fungal), "0", inv.fungal)) ->
  BTdata_combined
#> Joining, by = "pid"
#> Joining, by = "pid"

BTdata_combined %>% 
  select(-arbovirus) -> df.sum.tbl



# Make the summary table
bind_rows(
# generate summary stats and differences for categorical variables
  left_join(
    # Summary stats by groups
    df.sum.tbl %>% 
      select(where(is.character)) %>% 
      select(-pid) %>% 
      pivot_longer(-d28_death) %>% 
      filter(!is.na(d28_death)) %>% 
      dplyr::group_by(name,d28_death) %>%
      summarise(str = paste0(sum(value == 1, na.rm = TRUE), 
                             " (",
                             sp_dc(100*sum(value == 1, na.rm = TRUE)/ 
                                     sum(!is.na(value)),0),
                             "%)")) %>% 
      pivot_wider(id_cols = "name", 
                  names_from = "d28_death",
                  values_from = "str"),
    # Bootstrap difference in proportions 
    df.sum.tbl %>%
      select(where(is.character)) %>% 
      select(-pid) %>% 
      pivot_longer(-d28_death) %>% 
      filter(!is.na(d28_death)) %>% 
      dplyr::group_by(name) %>% 
    dplyr::summarise(bs = list(boot.two.bca(value, 
                                            d28_death, 
                                            propz))) %>%
      mutate(diff = map_dbl(bs, "Observed"),
             diff.lci = map_dbl(bs, 
                                function(x) unlist(
                                  (x["Confidence.limits"][[1]][[1]]))),
              diff.uci = map_dbl(bs, 
                                 function(x) unlist(
                                   (x["Confidence.limits"][[1]][[2]])))) %>% 
      select(-bs) %>% 
      mutate(diff.str = paste0(sp_dc(diff *100,0),
                             "% (", 
                             sp_dc(diff.lci*100,0),
                             " to ",
                             sp_dc(diff.uci*100,0),
                             "%)")) ,
    by = "name"),
      
  # for numeric variables
  left_join(
    # bootstrap differences
      df.sum.tbl %>% 
        select(where(is.numeric) | contains("d28_death")) %>% 
        pivot_longer(-d28_death) %>%
        filter(!is.na(d28_death) & !is.na(value)) %>% 
        dplyr::group_by(name) %>%
        dplyr::summarise(bs = list(boot.two.per(value, d28_death, median))) %>%
        mutate(diff = map_dbl(bs, "Observed"),
               diff.lci = 
                 map_dbl(
                   bs,function(x) unlist((x["Confidence.limits"][[1]][[1]]))
                         ),
                diff.uci = 
                 map_dbl(
                   bs, function(x) unlist((x["Confidence.limits"][[1]][[2]]))
                   )
               ) %>% 
        select(-bs) %>% 
        mutate(diff.str = paste0(sp_dc(diff,1),
                             " (", 
                             sp_dc(diff.lci,1),
                             " to ",
                             sp_dc(diff.uci,1),
                             ")")
        ),
      # make summary stats for two groups
      df.sum.tbl %>% 
        select(where(is.numeric) | contains("d28_death")) %>%
        pivot_longer(-d28_death) %>% 
        filter(!is.na(d28_death)) %>%
        dplyr::group_by(name, d28_death) %>% 
        dplyr::summarise( median = median(value, na.rm = T),
                          LQ = quantile(value, 0.25, na.rm = T),
                          UQ = quantile(value, 0.75, na.rm = T)) %>% 
        mutate(med_str = paste0(sp_dc(median, 1), " (",
                                sp_dc(LQ, 1), "-",
                                sp_dc(UQ, 1) ,")")) %>% 
        select(name, d28_death, med_str) %>% 
        pivot_wider(names_from = d28_death, values_from = med_str) 
  ) 
) -> p
#> `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.
#> `summarise()` has grouped output by 'name'. You can override using the `.groups` argument.
#> Joining, by = "name"
  


p$boldcol <- FALSE 
p$boldcol[(p$diff.lci >= 0 & p$diff.uci > 0) | 
            (p$diff.lci < 0 & p$diff.uci <= 0)] <- TRUE



p[match(c("calc_age", "ptsex", "hivstatus", "cd4_absolute", 
          "haemoglobin","screentemp", "t0hr", "t0sbp", "t0dbp", 
        "t0rr", "t0spo2","gcs", "ustand", "lactate" , 
        "wcc", "platelets", "sodium", "co2", "urea", 
        "creatinine", "inv.bacterial", "tb", "malaria", 
        "inv.fungal", "chik", "dengue","no_diagnosis",
        "abx", "time_to_abx", "fung.rx", "mal.rx", "tb.rx",
        "fluid.6hr"), p$name),] -> p


p <- p[c("name", "1", "0", "diff.str", "boldcol")]
names(p) <- c("Variable", "Died", "Survived", "Difference", "boldcol")

boldrows <- which(p$boldcol %in% TRUE)

p$Variable[p$Variable == "calc_age"] <- "Age (years)"
p$Variable[p$Variable == "ptsex"] <- "Male sex"
p$Variable[p$Variable == "hivstatus"] <- "HIV Infected"
p$Variable[p$Variable == "haemoglobin"] <- "Haemoglobin (x10^9^} g/dL)"
p$Variable[p$Variable == "cd4_absolute"] <- "CD4 count 10^6^ /L"
p$Variable[p$Variable == "screentemp"] <- "Temperature (C)"
p$Variable[p$Variable == "t0hr"] <- "Heart rate (beats/min)"
p$Variable[p$Variable == "t0sbp"] <- "Systolic BP (mmHg)"
p$Variable[p$Variable == "t0dbp"] <- "Diastolic BP (mmHg)"
p$Variable[p$Variable == "t0rr"] <- "Respiratory rate (breaths/min)"
p$Variable[p$Variable == "t0spo2"] <- "Oxygen saturation (%)"
p$Variable[p$Variable == "gcs"] <- "GCS"
p$Variable[p$Variable == "ustand"] <- "Unable to stand"
p$Variable[p$Variable == "lactate"] <- "Lactate (mmol/L)"
p$Variable[p$Variable == "wcc"] <- "White cell count (x10^9^)"
p$Variable[p$Variable == "platelets"] <- "Platelet count (x10^9^ /L)"
p$Variable[p$Variable == "sodium"] <- "Sodium (mmol /L)"
p$Variable[p$Variable == "co2"] <- "Bicarbonate (mmol /L)"
p$Variable[p$Variable == "urea"] <- "Urea (mmol /L)"
p$Variable[p$Variable == "creatinine"] <- "Creatinine (mmol /L)"

p$Variable[p$Variable == "inv.bacterial"] <- "Invasive bacterial infection"
p$Variable[p$Variable == "tb"] <- "TB"
p$Variable[p$Variable == "malaria"] <- "Malaria"
p$Variable[p$Variable == "inv.fungal"] <- "Invasive fungal infection"
p$Variable[p$Variable == "chik"] <- "Chikungunya"
p$Variable[p$Variable == "dengue"] <- "Dengue"
p$Variable[p$Variable == "no_diagnosis"] <- "No diagnosis"

p$Variable[p$Variable == "abx"] <- "Antibacterials"
p$Variable[p$Variable == "time_to_abx"] <- "Time to Antibacterials (hr)"
p$Variable[p$Variable == "fung.rx"] <- "Antifungals"
p$Variable[p$Variable == "mal.rx"] <- "Antimalarials"
p$Variable[p$Variable == "tb.rx"] <- "Antimycobacterials"

p$Variable[p$Variable == "fluid.6hr"] <- "IV fluid over 6hr (L)"



kbl(select(p,-boldcol), 
      row.names = F, caption = "TABLE 5: Univariable associations with death by 28 days. Numeric variables are presented as median (IQR) and categorical variables as proportions. Difference column shows difference in medians or difference in proportions with 95% confidence intervals. Variables shown in bold are those for which the 95% confidence intervals do not cross 0." ) %>%
 kable_classic(full_width = FALSE) %>%
  row_spec(boldrows, bold = T) %>%
  pack_rows("Host Variables", 1,5, bold = F) %>% 
   pack_rows("Severity Variables", 6, 20, bold = F)  %>%
    pack_rows("Diagnosis", 21,27, bold = F) %>% 
   pack_rows("Treatment Received", 28, 33, bold = F)

TABLE 5: Univariable associations with death by 28 days. Numeric variables are presented as median (IQR) and categorical variables as proportions. Difference column shows difference in medians or difference in proportions with 95% confidence intervals. Variables shown in bold are those for which the 95% confidence intervals do not cross 0.
Variable	Died	Survived	Difference
Host Variables
Age (years)	36.4 (31.5-46.0)	35.9 (27.4-42.9)	-0.5 (-8.1 to 3.2)
Male sex	19 (49%)	93 (51%)	2% (-15 to 19%)
HIV Infected	27 (75%)	116 (67%)	-6% (-21 to 12%)
CD4 count 10⁶ /L	41.0 (17.0-224.5)	188.0 (69.0-302.0)	147.0 (45.0 to 185.0)
Haemoglobin (x10⁹} g/dL)	9.1 (6.0-10.4)	11.0 (8.6-13.4)	1.9 (0.8 to 3.9)
Severity Variables
Temperature (C)	38.1 (37.7-38.8)	38.5 (38.0-39.0)	0.4 (0.0 to 0.7)
Heart rate (beats/min)	123.0 (104.5-138.5)	120.0 (102.0-131.0)	-3.0 (-13.0 to 6.0)
Systolic BP (mmHg)	89.0 (76.0-120.5)	99.0 (86.5-118.5)	10.0 (-1.0 to 16.0)
Diastolic BP (mmHg)	60.0 (51.5-80.5)	67.0 (57.0-75.5)	7.0 (1.0 to 14.0)
Respiratory rate (breaths/min)	34.0 (32.0-36.5)	34.0 (32.0-38.0)	0.0 (-2.0 to 2.0)
Oxygen saturation (%)	95.0 (89.5-97.0)	97.0 (95.0-98.0)	2.0 (0.0 to 3.0)
GCS	15.0 (15.0-15.0)	15.0 (15.0-15.0)	0.0 (0.0 to 0.0)
Unable to stand	27 (69%)	36 (20%)	-50% (-64 to -33%)
Lactate (mmol/L)	4.9 (3.0-10.6)	3.2 (2.1-4.5)	-1.7 (-6.3 to -0.1)
White cell count (x10⁹)	5.9 (3.5-11.0)	6.9 (4.6-11.5)	1.0 (-1.4 to 2.8)
Platelet count (x10⁹ /L)	181.5 (86.8-300.8)	223.0 (148.0-296.5)	41.5 (-33.5 to 73.0)
Sodium (mmol /L)	131.0 (127.0-137.8)	134.0 (130.0-137.0)	3.0 (-1.0 to 6.0)
Bicarbonate (mmol /L)	17.0 (14.0-21.0)	20.0 (17.0-22.0)	3.0 (0.0 to 4.5)
Urea (mmol /L)	7.8 (4.5-14.3)	4.5 (3.2-7.0)	-3.3 (-8.6 to -1.2)
Creatinine (mmol /L)	90.0 (60.0-185.0)	73.0 (59.0-96.0)	-17.0 (-48.0 to 7.0)
Diagnosis
Invasive bacterial infection	5 (13%)	32 (17%)	5% (-10 to 15%)
TB	15 (38%)	61 (33%)	-5% (-23 to 11%)
Malaria	0 (0%)	21 (11%)	11% (7 to 16%)
Invasive fungal infection	3 (8%)	2 (1%)	-7% (-19 to -1%)
Chikungunya	2 (5%)	15 (8%)	3% (-9 to 9%)
Dengue	2 (5%)	12 (7%)	1% (-10 to 7%)
No diagnosis	18 (46%)	64 (35%)	-11% (-29 to 5%)
Treatment Received
Antibacterials	37 (95%)	167 (91%)	-4% (-10 to 7%)
Time to Antibacterials (hr)	4.7 (3.8-8.8)	5.3 (3.6-10.8)	0.6 (-1.1 to 1.7)
Antifungals	7 (18%)	19 (10%)	-8% (-23 to 3%)
Antimalarials	0 (0%)	12 (7%)	7% (3 to 10%)
Antimycobacterials	4 (10%)	49 (27%)	17% (3 to 26%)
IV fluid over 6hr (L)	1.4 (1.0-2.0)	1.3 (0.6-2.0)	-0.1 (-0.7 to 0.2)


# KM curve 
# Models to estimate HR are fit below

psurv2 <- ggsurvplot(
  survfit(Surv(t,died) ~ hivstatus,
          data = subset(BTparticipants, !is.na(hivstatus))), 
          conf.int = F, xlim = c(0, 190), 
          ylim = (c(0.6,1)), ggtheme = theme_bw(), 
          size = 0.5, 
          break.time.by = 30, 
          linetype = c("strata"),
          censor.shape = 124,
          censor.size = 2.5,
          pval = "HR 2.0 (95% CrI 1.1-4.0) HIV+ vs HIV-", 
          pval.coord = c(15,0.65), pval.size = 4
)

psurv2 <- psurv2$plot

psurv2 + 
  theme(legend.position = "right", 
        legend.title = element_blank()) +   
  scale_color_manual(labels = c("HIV-", "HIV+"), 
                     values = viridis(6, option = "C")[c(1,4)]) + 
  scale_linetype_manual(labels = c("HIV-", "HIV+"),
                        values = c("dashed", "solid")) +
  labs(x = "Time post enrollment (days)") -> psurv2

psurv2
#> Warning: Removed 10 row(s) containing missing values (geom_path).
#> Warning: Removed 9 rows containing missing values (geom_point).

FIGURE 2: Kaplan-Meier survival plot showing hazard ratio of death for HIV infected vs noninfected participants from Bayesian cox proportional hazards model with 95% credible intervals.

if (write_figs) {
 
ggsave(here("figures/MAIN_F2_KM_plot.pdf"), psurv2, width = 6, height = 4)
ggsave(here("figures/MAIN_F2_KM_plot.tiff"), psurv2, width = 6, height = 4,
       dpi = 600)
  
   
}


# Fit Cox model

brm(t | cens(1-died) ~ 1 + hivstatus, 
    data = subset(BTparticipants, !is.na(hivstatus)),
    family = brmsfamily("cox")) -> m.b.cox 
#> Compiling Stan program...
#> Trying to compile a simple C file
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Start sampling
#> 
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mcmc_intervals_data(m.b.cox, 
                    regex_pars = "^b_", 
                    transformations = exp, 
                    prob_outer = 0.95)
#> # A tibble: 2 x 9
#>   parameter      outer_width inner_width point_est    ll     l     m     h    hh
#>   <fct>                <dbl>       <dbl> <chr>     <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 t(b_Intercept)        0.95         0.5 median    0.157 0.262 0.334 0.429 0.697
#> 2 t(b_hivstatus…        0.95         0.5 median    1.11  1.64  2.02  2.55  4.05


df.sum.tbl %>% 
  select(d28_death, malaria,dengue, chik, 
         inv.bacterial,
         inv.fungal, tb, no_diagnosis) %>% 
  pivot_longer(-d28_death) %>% 
  filter(!is.na(d28_death), value == 1) %>% 
  group_by(name) %>%
  mutate(name = recode(name,
                       tb = "Tuberculosis",
                       inv.fungal = "Invasive fungal infection",
                       dengue = "Dengue",
                       chik = "Chikungunya",
                       no_diagnosis = "No diagnosis",
                       inv.bacterial = "Invasive bacterial infection",
                       malaria = "Malaria"),
         d28_death = as.numeric(d28_death)) %>% 
  summarise(mort = sum(d28_death)/ length(d28_death),
            lci = binom.test(sum(d28_death), 
                             length(d28_death))$conf.int[1],
            uci = binom.test(sum(d28_death), 
                             length(d28_death))$conf.int[2]) %>% 
  mutate(name = fct_reorder(name, mort)) %>% 
  ggplot(aes(name,mort, ymin = lci, ymax = uci)) +
  geom_point() +
  geom_errorbar(width = 0) +
  coord_flip() +
  labs(x = "", y = "Mortality") +
  theme_bw() -> paetiol

paetiol

SUPPLEMENTARY FIGURE 7: Mortality stratified by diagnosis

if (write_figs) {
  
  ggsave(
    here("figures/SUP_F7_mort_by_dx.pdf"),
    paetiol,
    width = 5,
    height = 3,
    units = "in"
  )
  
  ggsave(
    here("figures/SUP_F7_mort_by_dx.tiff"),
    paetiol,
    width = 5,
    height = 3,
    units = "in",
    dpi = 600
  ) 
  
}

Modelling determinents of outcome

Missing data

Plot missing data priot to imputing missing data with the mice package (below).


# Plot missing data


BTparticipants %>%
  select(
    calc_age,
    ptsex,
    hivstatus,
    cd4_absolute,
    hivonart,
    hivcpt,
    ever_tb,
    screentemp,
    t0hr,
    t0rr,
    t0sbp,
    t0dbp,
    t0spo2,
    gcs,
    ustand,
    days_unwell,
    haemoglobin,
    platelets,
    wcc,
    sodium,
    potassium,
    co2,
    creatinine,
    urea,
    lactate,
    d28_death,
    d90_death,
    d180_death
  ) %>%
  rename(
    Age = calc_age,
    CD4 = cd4_absolute,
    Lactate = lactate,
    Creatinine = creatinine,
    Urea = urea,
    Platelets = platelets,
    SpO2 = t0spo2,
    SBP = t0sbp,
    DBP = t0dbp,
    GCS = gcs,
    Temperature = screentemp,
    WCC = wcc,
    RR = t0rr,
    Haemoglobin = haemoglobin,
    HIV_status = hivstatus,
    Current_ART = hivonart,
    Taking_CPT = hivcpt,
    Previous_TB = ever_tb,
    HR = t0hr,
    Sex = ptsex,
    Sodium = sodium,
    Bicarbonate = co2,
    Able_to_stand = ustand,
    Length_of_illness = days_unwell
  ) %>%
  mutate(across(
    matches("ART|CPT|CD4"),
    ~ case_when(
      HIV_status == "Non reactive" ~ "Not done (HIV-)",
      TRUE ~ as.character(.x)
    )
  )) %>%
  mutate(across(
    everything(),
    ~
      case_when(
        is.na(.x) ~ "Missing",
        .x == "Not done (HIV-)" ~ "Not done (HIV-)",
        TRUE ~ "Not missing"
      )
  )) %>%
  pivot_longer(everything()) %>%
  group_by(name) %>%
  mutate(sortvar = sum(value == "Not missing")) %>%
  ggplot(aes(fct_reorder(name, sortvar), fill = value)) +
  geom_bar() +
  coord_flip() +
  scale_fill_manual(values = viridis_pal()(6)[c(2, 4, 5)]) +
  labs(y = "Number of participants",
       x = "Variable") +
  theme_bw() +
  theme(legend.title = element_blank()) -> p.miss

p.miss

$SUPPLEMENTARY FIGURE 10: Missing data by variable. HIV related variables were not available for HIV-noninfected participants, recorded here as \'Not done (HIV-)\'. SpO2 = capilliary oxygen saturation, SBP/DBP = systolic/diastolic blood pressure, RR = respiratory rate, TB = tuberculosis, WCC = white cell count, ART = antiretroviral therapy, CPT = cotrimoxazole preventative therapy.$

SUPPLEMENTARY FIGURE 10: Missing data by variable. HIV related variables were not available for HIV-noninfected participants, recorded here as 'Not done (HIV-)'. SpO2 = capilliary oxygen saturation, SBP/DBP = systolic/diastolic blood pressure, RR = respiratory rate, TB = tuberculosis, WCC = white cell count, ART = antiretroviral therapy, CPT = cotrimoxazole preventative therapy.

if (write_figs) {
  
ggsave(here("figures/SUP_F10_miss_data.tiff"),p.miss, width = 6, height = 4, units = "in",dpi = 600)
ggsave(here("figures/SUP_F10_miss_data.pdf"),p.miss, width = 6, height = 4, units = "in")
  
}

Transform data and plot correlation matrix


# Transform variables 


BTparticipants %>%
  select(
    ustand,
    calc_age,
    ptsex,
    hivstatus,
    cd4_absolute,
    haemoglobin,
    screentemp,
    t0sbp,
    t0dbp,
    t0hr,
    t0rr,
    t0spo2,
    gcs,
    lactate,
    wcc,
    platelets,
    sodium,
    co2,
    creatinine,
    urea
  ) %>%
  mutate(
    male = if_else(ptsex == "Male", 1, 0),
    hiv_reactive = case_when(
      hivstatus == "Reactive" ~ 1,
      hivstatus == "Non reactive" ~ 0,
      TRUE ~ NA_real_
    ),
    gcs.low = as.numeric(gcs < 15)
  ) %>%
  select(-c(ptsex, hivstatus, gcs))  ->
  df.mod.trans
df.mod.trans[df.mod.trans== 999] <- NA



df.mod.trans %>%
  mutate(
    calc_age_log = log(calc_age),
    cd4_absolute_log = log(cd4_absolute),
    creatinine_log = log(creatinine),
    lactate_log = log(lactate),
    plt_log = log(platelets),
    t0spo2_log = log(101 - t0spo2),
    t0sbp_log = log(t0sbp),
    t0dbp_log = log(t0dbp),
    urea_log = log(urea),
    screentemp_log = log(41 - screentemp),
    wcc_log = log(wcc),
    sodium_log = log(sodium),
    t0rr_log = log(t0rr)
  ) -> df.mod.trans

df.mod.trans %>%
  pivot_longer(everything()) %>%
  ggplot(aes(value)) +
  geom_density() +
  facet_wrap( ~ name, scales = "free")
#> Warning: Removed 80 rows containing non-finite values (stat_density).

Density plots of transformed and untransformed variables


# drop unused vars
df.mod.trans %>%
  select(
    -c(
      cd4_absolute,
      creatinine,
      lactate,
      platelets,
      t0spo2,
      t0sbp,
      t0dbp,
      t0rr_log,
      screentemp,
      urea,
      t0dbp,
      sodium,
      wcc,
      calc_age
    )
  ) -> df.mod.trans


# Generate and plot correlation matrix

df.mod.trans %>%
  select(-c(ustand,
            hiv_reactive,
            gcs.low,
            male)) %>%
  dplyr::rename(
    log_Age = calc_age_log,
    log_CD4 = cd4_absolute_log,
    log_Lactate = lactate_log,
    log_Cr = creatinine_log,
    log_Plt = plt_log,
    log_SpO2 = t0spo2_log,
    log_SBP = t0sbp_log,
    Temp = screentemp_log,
    log_WCC = wcc_log,
    RR = t0rr,
    Hb = haemoglobin,
    HR = t0hr,
    log_Na = sodium_log,
    `HCO3-` = co2,
    log_DBP = t0dbp_log,
    log_Urea = urea_log
  ) %>%
  cor(., use = "pairwise") %>%
  as.data.frame() %>%
  pheatmap()

SUPPLEMENTARY FIGURE 8: Correlation matrix of included continuous variables (using Pearson’s correlation coefficient), row- and column-clustered using hierarchical clustering, showing very strong correlation between creatinine and urea and systolic and diastolic blood pressure. Also note negative correlation (though less strong) between renal failure and thrombocytopenia and low bicarbonate. Plt = Platelet count, WCC= White cell count, HCO3- = serum bicarbonate, SBP = systolic blood pressure, DBP = diastolic blood pressure, Hb = Haemoglobin, CD4 = CD4 cell count, RR = respiratory rate, Na = Serum sodium, Temp = Temperature, SpO2 = Capillary oxygen saturation, HR = Heart rate, Cr = Creatinine. Prefix log_ indicates log transformation of variable.


#ggsave("figures/SUP_var_correlation_matrix.jpeg", 
# as.ggplot(p.hm), height = 5, width = 6, units = "in")


# rename and drop diastolic blood pressure and urea
# because strongly correlated with systolic blood pressure and creatinine

df.mod.trans %>%
  dplyr::rename(
    Age = calc_age_log,
    CD4 = cd4_absolute_log,
    Lactate = lactate_log,
    Cr = creatinine_log,
    Plt = plt_log,
    SpO2 = t0spo2_log,
    SBP = t0sbp_log,
    Temp = screentemp_log,
    WCC = wcc_log,
    RR = t0rr,
    Hb = haemoglobin,
    `HIV+` = hiv_reactive,
    HR = t0hr,
    Male = male,
    `GCS<15` = gcs.low,
    Na = sodium_log,
    `HCO3-` = co2,
    CantStand = ustand
  ) %>%
  select(-c(t0dbp_log,
            urea_log)) ->
  df.mod.trans

Perform PCA


# do PCA

# first prepare full df with all necessary covariates for modelling

# Prepare data frame with all metaadata --------------------------------
# remember df.mod.trans is all the transformed covariates for PCA
# BTdata_combined is all data


BTparticipants %>%
  select(-c(
    d90_death,
    d180_death,
    t,
    died,
    art_time,
    hivcpt,
    ever_tb,
    tbongoing
  )) %>%
  left_join(
    BTtreatment %>%
      transmute(
        pid = pid,
        tb.rx = !is.na(timeto_antitb),
        fung.rx = !is.na(timeto_antifungal),
        mal.rx = !is.na(timeto_antimalarial),
        time_to_abx = timeto_ab,
        fluid.6hr = iv_fluid_6hr
      )
  ) %>%
  left_join(
    select(
      BTaetiology,
      pid,
      malaria,
      dengue,
      chik,
      arbovirus,
      inv.bacterial,
      inv.fungal,
      tb
    ) %>%
      mutate(across(!contains("pid"), ~ if_else(is.na(
        .x
      ), 0, .x)))
  ) %>%
  mutate(
    ustand = as.character(ustand),
    malaria = as.character(malaria),
    dengue = as.character(dengue),
    chik = as.character(chik),
    inv.bacterial = as.character(inv.bacterial),
    inv.fungal = as.character(inv.fungal),
    tb = as.character(tb),
    tb.rx = as.character(as.numeric(tb.rx)),
    fung.rx = as.character(as.numeric(fung.rx)),
    mal.rx = as.character(as.numeric(mal.rx)),
    time_to_abx = as.numeric(time_to_abx),
    abx = as.character(as.numeric(!is.na(time_to_abx))),
    fluid.6hr = fluid.6hr / 1000,
    d28_death = as.character(d28_death),
    ptsex = recode(ptsex,
                   "Male" = "1",
                   "Female" = "0"),
    hivstatus = recode(
      hivstatus,
      "Reactive" = "1",
      "Non reactive" = "0",
      .default = NA_character_
    ),
  ) %>%
  mutate(
    no_diagnosis = 1,
    no_diagnosis = case_when(
      malaria == 1 ~ 0,
      dengue == 1 ~ 0,
      chik == 1 ~ 0,
      inv.bacterial == 1 ~ 0,
      inv.fungal == 1 ~ 0,
      tb == 1 ~ 0,
      TRUE ~ no_diagnosis
    ),
    no_diagnosis = as.character(no_diagnosis),
    tb = if_else(is.na(tb), "0", tb),
    malaria = if_else(is.na(malaria), "0", malaria),
    dengue = if_else(is.na(dengue), "0", dengue),
    chik = if_else(is.na(chik), "0", chik),
    inv.bacterial = if_else(is.na(inv.bacterial), "0", inv.bacterial),
    inv.fungal = if_else(is.na(inv.fungal), "0", inv.fungal)
  ) ->
  BTdata_combined
#> Joining, by = "pid"
#> Joining, by = "pid"

# do PCA -----------------------------------------------------------------
# on df,mod.trans

prcomp(df.mod.trans[complete.cases(df.mod.trans),],
       scale = TRUE) -> p
  p.scores <- as.data.frame(p$scores)

# add in metadata: 28 day death  
  
bind_cols(
  BTdata_combined[complete.cases(df.mod.trans),] %>% 
    select(d28_death),
  as.data.frame(p$x)
) -> p.out




## contribution of different vars to PCA coord  -plot ----------------------- 



fviz_contrib(p, choice = "var", axes = 1) +
  theme(plot.title = element_blank()) -> p.cont1
fviz_contrib(p, choice = "var", axes = 2) +
  theme(plot.title = element_blank())  -> p.cont2
fviz_contrib(p, choice = "var", axes = 3) +
  theme(plot.title = element_blank())  -> p.cont3

ggbiplot_mod(p, circle = TRUE, choices = 3:4) + theme_bw() -> p.pca.3.4

((p.cont1 / p.cont2 / p.cont3) | p.pca.3.4) + 
  plot_layout(widths = c(1,2)) +
  plot_annotation(tag_levels  = "A") -> supp.pca.plots

# Points projected onto PCA space


p.out %>% 
  filter(!is.na(d28_death)) %>% 
  ggplot(aes(PC1, PC2, fill = as.factor(d28_death),
             color = as.factor(d28_death), 
             shape = as.factor(d28_death),
             alpha = as.factor(d28_death))) +
  geom_point(
  # pch = 21,
    size = 2,
   # alpha = 0.3
  ) +
  theme_bw() +
  scale_color_manual(
    values = c(viridis(6, option = "C")[4],"black"),
    labels = c("Survived", "Died"),
    name = "28 day outcome"
  ) +
  scale_alpha_manual(
    values = c(0.4,1), 
    labels = c("Survived", "Died"),
    name = "28 day outcome"
  ) +
  scale_fill_manual(
    values = viridis(6, option = "C")[c(4, 1)],
    labels = c("Survived", "Died"),
    name = "28 day outcome"
  ) +
    scale_shape_manual(values = c(23,21),
    labels = c("Survived", "Died"),
    name = "28 day outcome" ) +
  geom_vline(aes(xintercept = 0)) +
  geom_hline(aes(yintercept = 0)) +
  theme(legend.position = "top")  -> p5

ggbiplot_mod(p, circle = TRUE, labels.size = 1) + 
  theme_bw() -> p1

(p1  | p5 )  + 
  plot_annotation(tag_levels = "A")

A: Host-severity principal components PC1 and PC2, B: points projected onto PCA space


supp.pca.plots

SUPPLEMENTARY FIGURE 9: Original host-severity variable contributions to PC1 (A), PC2 (B) and PC3 (C) and original variables projected onto PC3 and PC4 (D).


if (write_figs) {
  ggsave(
   here("figures/SUP_F9_pca_varplot.pdf"),
   ricks_titre_plot, width = 8, height = 8)
 
  ggsave(
   here("figures/SUP_F9_pca_varplot.tiff"),
   ricks_titre_plot, width = 8, height =8 )
}

Use leave-one-out cross validation to identify number of principal components to take forward for further modelling

# set prior

priors <- c(
  prior(student_t(3, 0, 2.5), class = "Intercept"),
  prior(student_t(3, 0, 2.5), class = "b")
)

# Fit Bayesian logistic regression models to predict death using 
# between 3-5 PC components

brm(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 +
    PC4 + PC5, prior = priors,
  data = p.out,
  family = bernoulli(link = "logit"), save_all_pars = TRUE
)-> b.mod1
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.6e-05 seconds
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#> Chain 2: 
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#> Chain 4:

b.mod1 <- add_criterion(b.mod1, "loo", moment_match = TRUE,reloo = TRUE)

brm(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 +
    PC4 ,
  data = p.out, prior = priors,
  family = bernoulli(link = "logit"), save_all_pars = TRUE
)-> b.mod2
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.5e-05 seconds
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#> Chain 2: 
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#> Chain 3:                0.113905 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.108668 seconds (Total)
#> Chain 4:

b.mod2 <- add_criterion(b.mod2, "loo", moment_match = TRUE,reloo = TRUE)

brm(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3,
  data = p.out, prior = priors,
  family = bernoulli(link = "logit"), save_all_pars = TRUE
)-> b.mod3
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.0529 seconds (Sampling)
#> Chain 1:                0.107412 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3:                0.104275 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4:

b.mod3 <- add_criterion(b.mod3, "loo", moment_match = TRUE,reloo = TRUE)

brm(
  formula = d28_death ~ PC1 +
    PC2,
  data = p.out,
  prior = priors,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
  
) -> b.mod4
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.052977 seconds (Sampling)
#> Chain 1:                0.104681 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 2:                0.052438 seconds (Sampling)
#> Chain 2:                0.100168 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:                0.047829 seconds (Sampling)
#> Chain 3:                0.094947 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:

b.mod4 <-
  add_criterion(b.mod4, "loo", moment_match = TRUE, reloo = TRUE)

loo_compare(b.mod1, b.mod2, b.mod3, b.mod4)
#>        elpd_diff se_diff
#> b.mod3  0.0       0.0   
#> b.mod2 -0.9       0.2   
#> b.mod4 -0.9       3.2   
#> b.mod1 -1.6       1.7

# save model comparison
#write_rds(b.mod1, "models/b.mod1.RDS")
#write_rds(b.mod2, "models/b.mod2.RDS")
#write_rds(b.mod3, "models/b.mod3.RDS")
#write_rds(b.mod4, "models/b.mod4.RDS")
#as.data.frame(loo_compare(b.mod1, b.mod2, b.mod3, b.mod4)) -> loo.df
#loo.df
#write.csv(loo.df, "tables/SUP_loo_mod_compare_df.csv")

Impute missing data

# Scale fluid and time to abx --------------------------------------------

fluid.mean <- mean(BTdata_combined$fluid.6hr, na.rm = TRUE)
fluid.sd <- sd(BTdata_combined$fluid.6hr, na.rm = TRUE)
BTdata_combined$fluid.6hr <- (BTdata_combined$fluid.6hr - 
                                fluid.mean)/fluid.sd
tta.mean <- mean(BTdata_combined$time_to_abx, na.rm = TRUE)
tta.sd <- sd(BTdata_combined$time_to_abx, na.rm = TRUE)
BTdata_combined$time_to_abx <- (BTdata_combined$time_to_abx- 
                                  tta.mean)/tta.sd

# Prepare datafrane for imputation

bind_cols(
  df.mod.trans,
  BTdata_combined %>% 
    select(
      d28_death,
      pid,
      tb.rx,
      fung.rx,
      mal.rx,
      malaria, 
      arbovirus, 
      inv.bacterial, 
      inv.fungal, 
      tb,
      time_to_abx,
      fluid.6hr)) %>% 
  mutate(
    CantStand = as.character(CantStand),
    `HIV+` = as.factor(`HIV+`),
    `GCS<15` = as.factor(`GCS<15`),
    d28_death = as.factor(d28_death),
    tb = as.numeric(tb),
    tb = if_else(is.na(tb), 0, tb),
    malaria = as.numeric(malaria),
    malaria = if_else(is.na(malaria), 0, malaria),
    arbovirus = as.numeric(arbovirus),
    arbovirus = if_else(is.na(arbovirus), 0, arbovirus),
    inv.bacterial = as.numeric(inv.bacterial),
    inv.bacterial = if_else(is.na(inv.bacterial), 0, inv.bacterial),
    inv.fungal = as.numeric(inv.fungal),
    inv.fungal = if_else(is.na(inv.fungal), 0, inv.fungal),
  ) %>%
  dplyr::rename(
    HIV_pos = `HIV+`,
    GCS_less15 = `GCS<15`,
    HCO3 = `HCO3-`
  ) %>% 
  as.data.frame() ->
  df.mod.trans.scale.plus.metadata

# make pred matrix ----------------------------------------------------------

# predict everything from everything else

mice(df.mod.trans.scale.plus.metadata, maxit = 0) -> ini
#> Warning: Number of logged events: 5
pm <- ini$predictorMatrix
ini$method -> meth
pm[, (ncol(df.mod.trans) + 2):ncol(df.mod.trans.scale.plus.metadata)] <- 0
pm[(ncol(df.mod.trans) + 2):ncol(df.mod.trans.scale.plus.metadata),] <- 0
meth[(ncol(df.mod.trans) + 2):ncol(df.mod.trans.scale.plus.metadata)] <- ""
df.mod.trans.scale.plus.metadata$time_to_abx <- as.numeric(
  df.mod.trans.scale.plus.metadata$time_to_abx
)

# impute missing data --------------------------------------------------------

m <- 10 # number of datasets
mice(df.mod.trans.scale.plus.metadata, m = m, predictorMatrix = pm,
     method = meth) -> df.mod.imp
#> 
#>  iter imp variable
#>   1   1  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   2  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   3  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   4  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   5  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   6  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   7  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   8  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   9  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   1   10  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   1  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   2  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   3  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   4  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   5  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   6  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   7  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   8  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   9  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   2   10  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   1  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   2  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   3  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   4  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   5  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   6  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   7  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   8  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   9  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   3   10  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   1  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   2  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   3  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   4  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   5  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   6  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   7  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   8  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   9  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   4   10  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   1  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   2  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   3  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   4  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   5  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   6  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   7  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   8  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   9  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
#>   5   10  Hb  RR  HCO3  HIV_pos  CD4  Cr  Lactate  Plt  SpO2  SBP  WCC  Na  d28_death
complete(df.mod.imp, action = "all") -> datasets.imp

Scale imputed data and project onto PCA coordinates

# get variables to correct type -----------------------------------------------

lapply(datasets.imp,
       function(x)
         x %>% mutate(
           CantStand = as.numeric(CantStand),
           HIV_pos = as.numeric(HIV_pos),
           GCS_less15 = as.numeric(GCS_less15),
           d28_death = as.numeric(d28_death),
           d28_death = d28_death - 1,
           tb = as.factor(tb),
           tb.rx = as.factor(tb.rx),
           malaria = as.factor(malaria),
           inv.fungal = as.factor(inv.fungal)
         ) %>%
         as.data.frame) -> datasets.imp

# project onto pca coords ----------------------------------------------------

lapply(datasets.imp,
       function(x)
         bind_cols(
           x,
           as.data.frame(
             scale(x[,1:ncol(df.mod.trans)],
                 p$center, p$scale) %*% p$rotation) 
         )) -> datasets.imp

# add the mice .imp and .id vars back in -------------------------------------

for (i in 1:m) {
    datasets.imp[[i]]$.imp <- i
    datasets.imp[[i]]$.id <- 1:nrow(datasets.imp[[i]])
  }

Fit models for antimicrobial therapy

# fit univariable and then multivariable models -------------------------------

# tb ----------------------------------------------------------------------

# univrariable - diagnosis

brm_multiple(
  formula = d28_death ~
    tb,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.tb.unadj
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> Chain 1: 
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#> Chain 2: 
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#> Chain 3: 
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 2
#> 
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#> Chain 1: 
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#> Chain 1: 
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#> Chain 2: 
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#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.2e-05 seconds
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#> Chain 3: 
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.2e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.12 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
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#> Fitting imputed model 8
#> 
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#> Chain 1: 
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#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> Start sampling
#> Fitting imputed model 10
#> 
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#> Start sampling
#write_rds(b.m.tb.unadj, "models/b.m.tb.unadj.RDS")

# univrariable - treatment

brm_multiple(
  formula = d28_death ~
    tb.rx,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.tb.rx.unadj
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.090168 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.093938 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1:                0.083047 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.091772 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.087814 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 10
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> 
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#> Chain 4: 
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#> Chain 4:
#> Start sampling
#write_rds(b.m.tb.rx.unadj, "models/b.m.tb.rx.unadj.RDS")

# multivariable

brm_multiple(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 +
    tb + tb.rx,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.tb
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 3.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
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#> Fitting imputed model 4
#> 
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Start sampling
#> Fitting imputed model 9
#> 
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#> Chain 1: 
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#> Fitting imputed model 10
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#> Start sampling
#write_rds(b.m.tb, "models/b.m.tb.final.RDS")


#mcmc_intervals_data(b.m.tb, regex_pars = "^b_", 
#transformations = exp, prob_outer = 0.95)

# malaria --------------------------------------------------------------------

# multivariable 

brm_multiple(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 +
    malaria + mal.rx,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.mal
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> Start sampling
#> Fitting imputed model 2
#> 
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.229743 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
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#> 
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#> Chain 4: 
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
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#> Fitting imputed model 8
#> 
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#> Start sampling
#> Fitting imputed model 9
#> 
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#> Start sampling
#> Fitting imputed model 10
#> 
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#> Start sampling
#write_rds(b.m.mal, "models/b.m.mal.final.RDS")

# univariable - diagnosis

brm_multiple(
  formula = d28_death ~
    malaria,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.mal.univ
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.29 seconds.
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#> Chain 1:                0.106873 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 4:                0.111459 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 4:                0.100273 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 1:                0.11341 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
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#> Chain 2: 
#> 
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#> Chain 3: 
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.104817 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.109033 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
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#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
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#> Chain 1: 
#> 
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#> Chain 2: 
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#> Start sampling
#> Fitting imputed model 10
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> Chain 3: 
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#> Start sampling
#write_rds(b.m.mal.univ, "models/b.m.mal.univ.RDS")

# univariable - treatment

brm_multiple(
  formula = d28_death ~
    mal.rx,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.mal.rx.univ
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.6e-05 seconds
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#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
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#> Chain 3: 
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#> Start sampling
#> Fitting imputed model 3
#> 
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#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.1072 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.09955 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.109661 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
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#> Fitting imputed model 8
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#> Start sampling
#> Fitting imputed model 9
#> 
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#> Fitting imputed model 10
#> 
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#> Start sampling
#write_rds(b.m.mal.rx.univ, "models/b.m.mal.rx.univ.RDS")
#mcmc_intervals_data(b.m.mal, regex_pars = "^b_",
#transformations = exp, prob_outer = 0.95)

# invasive fungal ----------------------------------------------------------

# multivariable

brm_multiple(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 +
    inv.fungal + fung.rx,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.fung
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.164313 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.149688 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.160146 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.3 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:                0.158061 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.1e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 4.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.45 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.4 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 10
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
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#> Chain 1: 
#> 
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#> Chain 2: 
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Chain 4: 
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#> Start sampling
#write_rds(b.m.fung, "models/b.m.fung.final.RDS")

# diagnosis

brm_multiple(
  formula = d28_death ~
    inv.fungal,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.fung.univ
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 3.8e-05 seconds
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.087961 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.08187 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1:                0.089603 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.099766 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.095717 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.24 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 4:                0.077987 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
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#> Chain 2: 
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#> Fitting imputed model 10
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#> Start sampling
#write_rds(b.m.fung.univ, "models/b.m.fung.univ.RDS")

brm_multiple(
  formula = d28_death ~ fung.rx,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.fung.rx.univ
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.039115 seconds (Sampling)
#> Chain 1:                0.090361 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3:                0.082256 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.077787 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
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#> Chain 1:                0.078841 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.28 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.08493 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.090669 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 2.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:                0.082219 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3.1e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.31 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.099529 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.041804 seconds (Sampling)
#> Chain 1:                0.083696 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:                0.089066 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.089105 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.27 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 10
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
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#> Start sampling
#write_rds(b.m.fung.rx.univ, "models/b.m.fung.rx.univ.RDS")

# make output df -----------------------------------------------------------

bind_rows(
  mcmc_intervals_data(
    b.mod3,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "_PC")) %>%
    mutate(type = "unadjusted",
           adj = "unadjusted"),
  mcmc_intervals_data(
    b.m.tb.unadj,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "tb")) %>%
    mutate(type = "unadjusted",
           adj = "unadjusted"),
  mcmc_intervals_data(
    b.m.tb.rx.unadj,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "tb")) %>%
    mutate(type = "unadjusted",
           adj = "unadjusted"),
  mcmc_intervals_data(
    b.m.tb,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "tb") |
             str_detect(parameter, "_PC")) %>%
    mutate(type = "tb.model",
           adj = "adjusted"),
  mcmc_intervals_data(
    b.m.mal.univ,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "mal")) %>%
    mutate(type = "unadjusted",
           adj = "unadjusted"),
  mcmc_intervals_data(
    b.m.mal.rx.univ,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "mal")) %>%
    mutate(type = "unadjusted",
           adj = "unadjusted"),
  mcmc_intervals_data(
    b.m.mal,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "mal") |
             str_detect(parameter, "_PC")) %>%
    mutate(type = "mal.model",
           adj = "adjusted"),
  mcmc_intervals_data(
    b.m.fung.univ,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "fung")) %>%
    mutate(type = "unadjusted",
           adj = "unadjusted"),
  mcmc_intervals_data(
    b.m.fung.rx.univ,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "fung")) %>%
    mutate(type = "unadjusted",
           adj = "unadjusted"),
  mcmc_intervals_data(
    b.m.fung,
    regex_pars = "^b_",
    transformations = exp,
    prob_outer = 0.95
  ) %>%
    filter(str_detect(parameter, "fung") |
             str_detect(parameter, "_PC")) %>%
    mutate(type = "fung.model",
           adj = "adjusted")
  
) -> df.plot


# Plot effects of antimicrobial treatments

df.plot %>% 
  filter(!(str_detect(parameter, "_PC") & adj == "adjusted")) %>% 
  filter((str_detect(parameter, "rx1") | str_detect(parameter, "PC"))) %>% 
  mutate(parameter =
           case_when(
             str_detect(parameter, "fung.rx") ~ "Antifungal",
             str_detect(parameter, "mal.rx") ~ "Antimalarial",
             str_detect(parameter, "tb.rx") ~ "Antimtubercular",
             TRUE ~ str_replace(parameter, "t\\(b_", "")
           )) %>% 
  mutate(parameter = str_replace(parameter, "\\)", "")
  ) %>% 
  ggplot(aes(parameter, 
             m, 
             ymin = ll, 
             ymax = hh, 
             color = fct_rev(adj),
             shape = fct_rev(adj))) + 
  geom_point(position = position_dodge(width = 0.4)) + 
  geom_errorbar(width = 0,position = position_dodge(width = 0.4)) + 
  theme_bw() +
  geom_hline(aes(yintercept = 1), linetype = "dashed") +
  labs(y = "OR", x = "Antimicrobial")  +
  theme(legend.position = "top", 
        legend.title = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)) +
  scale_color_manual(values = c(fill = 
                                viridis(6, option = "C")[4],
                                "black")) +
  scale_shape_manual(values = c(4, 16))-> p.effects

p.effects

Unadjusted and adjusted odds ratios for death for antimicrobial treatments.

Fit models for time-to-antibacterials and vlume of IV fluid

# models for time to antibacterials (linear and restricted cubic spline) ------
# and IV fluid (linear and restricted cubic spline) ------------------

# time to abx ----------------------------------------------------------

# linear time to abx univariable

brm_multiple(
  formula = d28_death ~   time_to_abx,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.tta.lin.univ
#> Compiling the C++ model
#> Warning: Rows containing NAs were excluded from the model.
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.26 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.3e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 3.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.37 seconds.
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 1: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
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#> Chain 3: 
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.1e-05 seconds
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.61 seconds.
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#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 10
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
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#> Chain 1: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling

#write_rds(b.m.tta.lin.univ, "models/b.m.tta.lin.univ.RDS")
#exp(mcmc_intervals_data(b.m.tta.lin.univ,  regex_pars = "^b",
#                        prob_outer = 0.95)[5:9]/tta.sd)

# linear time to abx corrected for severity/host

brm_multiple(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 +
    time_to_abx,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.tta.lin
#> Compiling the C++ model
#> Warning: Rows containing NAs were excluded from the model.
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 3.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.35 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
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#> Chain 2: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.116323 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.8e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.123902 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.062685 seconds (Sampling)
#> Chain 1:                0.130926 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.6e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.073565 seconds (Sampling)
#> Chain 1:                0.146549 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.8e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.2e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 10
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#write_rds(b.m.tta.lin, "models/b.m.tta.lin.RDS")
#exp(mcmc_intervals_data(b.m.tta.lin,  regex_pars = "^b",
#                    prob_outer = 0.95)[5,5:9]/tta.sd)



# restricted cubic spline time to abx
# calculate quantiles for knots
k <- quantile(datasets.imp[[1]]$time_to_abx,
              c(0.1, 0.5, 0.9),
              na.rm = TRUE)

# fit
brm_multiple(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 +
    ns(time_to_abx,
       knots = c(-0.599, -0.418, 1.028)),
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.tta.nonlin
#> Compiling the C++ model
#> Warning: Rows containing NAs were excluded from the model.
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.9e-05 seconds
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#> Chain 3: 
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#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
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#> Chain 3: 
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#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2e-05 seconds
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.237611 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.237345 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.2e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 4:
#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Warning: Rows containing NAs were excluded from the model.
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
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#> Fitting imputed model 9
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#> Start sampling
#write_rds(b.m.tta.nonlin, "models/b.m.tta.nonlin.RDS")

# IV fluid ---------------------------------------------------------- 
# restricted cubic spine, corrected for severity/host

# get knot locations
k <- quantile(datasets.imp[[1]]$fluid.6hr,
              c(0.1, 0.5, 0.9),
              na.rm = TRUE)

# fit

brm_multiple(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 + ns(fluid.6hr, knots = c(-1.564, -0.009, 1.410)),
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.fluid
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
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#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.1e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.2 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 3.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.8e-05 seconds
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#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.9e-05 seconds
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.281039 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 9
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.2e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.22 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 10
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.1e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.21 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 2.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 4:
#> Start sampling
#write_rds(b.m.fluid, "models/b.m.fluid.RDS")

# linear corrected for severity/host

brm_multiple(
  formula = d28_death ~ PC1 +
    PC2  +
    PC3 + fluid.6hr,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
) -> b.m.fluid.lin
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 3.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.34 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.7e-05 seconds
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 2.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.23 seconds.
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#> Chain 1: 
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#> Chain 2: 
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.5e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4: 
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
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#> Chain 1: 
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#> Chain 2: 
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#> Chain 4: 
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#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.9e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.5e-05 seconds
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.7e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.9e-05 seconds
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#> Start sampling
#> Fitting imputed model 8
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
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#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
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#> Fitting imputed model 9
#> 
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#> Fitting imputed model 10
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# univariable 

brm_multiple(
  formula = d28_death ~ fluid.6hr,
  prior = priors,
  data = datasets.imp,
  family = bernoulli(link = "logit"),
  save_all_pars = TRUE
  
) -> b.m.fluid.lin.univ
#> Compiling the C++ model
#> Running /Library/Frameworks/R.framework/Resources/bin/R CMD SHLIB foo.c
#> clang -I"/Library/Frameworks/R.framework/Resources/include" -DNDEBUG   -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/Rcpp/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/unsupported"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/BH/include" -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/src/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppParallel/include/"  -I"/Library/Frameworks/R.framework/Versions/4.0/Resources/library/rstan/include" -DEIGEN_NO_DEBUG  -DBOOST_DISABLE_ASSERTS  -DBOOST_PENDING_INTEGER_LOG2_HPP  -DSTAN_THREADS  -DBOOST_NO_AUTO_PTR  -include '/Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp'  -D_REENTRANT -DRCPP_PARALLEL_USE_TBB=1   -I/usr/local/include   -fPIC  -Wall -g -O2  -c foo.c -o foo.o
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:88:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:1: error: unknown type name 'namespace'
#> namespace Eigen {
#> ^
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/src/Core/util/Macros.h:613:16: error: expected ';' after top level declarator
#> namespace Eigen {
#>                ^
#>                ;
#> In file included from <built-in>:1:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/StanHeaders/include/stan/math/prim/mat/fun/Eigen.hpp:13:
#> In file included from /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Dense:1:
#> /Library/Frameworks/R.framework/Versions/4.0/Resources/library/RcppEigen/include/Eigen/Core:96:10: fatal error: 'complex' file not found
#> #include <complex>
#>          ^~~~~~~~~
#> 3 errors generated.
#> make: *** [foo.o] Error 1
#> Fitting imputed model 1
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
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#> Start sampling
#> Fitting imputed model 2
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
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#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.080963 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 3
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.9e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.073671 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.6e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 4
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1:                0.090133 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.4e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
#> Chain 3: Adjust your expectations accordingly!
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
#> Chain 4: Adjust your expectations accordingly!
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#> Chain 4:                0.075414 seconds (Total)
#> Chain 4:
#> Start sampling
#> Fitting imputed model 5
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
#> Chain 1: Adjust your expectations accordingly!
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.5e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.15 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.4e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.14 seconds.
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#> Chain 4:
#> Start sampling
#> Fitting imputed model 6
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.8e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
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#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 1.6e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.16 seconds.
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#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.3e-05 seconds
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#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.3e-05 seconds
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#> Start sampling
#> Fitting imputed model 7
#> 
#> SAMPLING FOR MODEL '589194c652fbe87487eef702b9d61cad' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.5e-05 seconds
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#> Fitting imputed model 10
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#> Start sampling

#write_rds(b.m.fluid.lin, "models/b.m.fluid.lin.RDS")
#write_rds(b.m.fluid.lin.univ, "models/b.m.fluid.lin.univ.RDS")
#exp(mcmc_intervals_data(b.m.fluid.lin.univ,  regex_pars = "^b",
#                        prob_outer = 0.95)[5:9]*1000/fluid.sd)
#exp(mcmc_intervals_data(b.m.fluid.lin,  regex_pars = "^b",
#                        prob_outer = 0.95)[5,5:9]*1000/fluid.sd)

# plot marginal effects -----------------------------------------------------

conditional_effects(b.m.tta.nonlin, effects = "time_to_abx") -> tta.ce

tta.ce$time_to_abx %>% 
  ggplot(aes((time_to_abx + tta.mean/tta.sd)*tta.sd, 
             estimate__, ymin = lower__, ymax = upper__)) + 
  geom_line() + 
  geom_ribbon(alpha = 0.3, fill = viridis(6, option = "C")[4])  + 
  coord_cartesian(ylim = c(0,0.3), xlim = c(0,50)) + 
  theme_bw() + labs(x = "Time (hrs) to antibacterial", 
                    y = "28-day mortality") -> p.tta

conditional_effects(b.m.fluid, effects = "fluid.6hr") -> fluid.ce

fluid.ce$fluid.6hr %>% 
  ggplot(aes((fluid.6hr + fluid.mean/fluid.sd)*(fluid.sd), 
             estimate__, ymin = lower__, ymax = upper__)) + geom_line() + 
  geom_ribbon(alpha = 0.3, fill = viridis(6, option = "C")[4])  + 
  coord_cartesian(ylim = c(0,0.3)) + 
  theme_bw() + 
  labs(x = "Fluid (L) over 6hr", y = "28-day mortality") -> p.fluid


(p.fluid | p.tta ) + plot_annotation(tag_levels = "A")

Marginal effect of volume of antrvenous fluid (A) and time to antibacterials (B) from nonlinear models

Final parameter estimate table

# Make table of model outputs
bind_rows(
  df.plot,
  mcmc_intervals_data(
    b.m.tta.lin.univ,
    regex_pars = "^b_",
    prob_outer = 0.95
  ) %>%
    dplyr::mutate(across(
      starts_with(c("l", "m", "h")),
      ~ case_when(parameter == "b_time_to_abx" ~
                    exp(.x / tta.sd),
                  TRUE ~ exp(.x))
    )) %>%
    filter(str_detect(parameter, "abx") |
             str_detect(parameter, "_PC")) %>%
    mutate(type = "unadjusted",
           adj = "unadjusted"),
  mcmc_intervals_data(b.m.tta.lin,
                      regex_pars = "^b_",
                      prob_outer = 0.95) %>%
    dplyr::mutate(across(
      starts_with(c("l", "m", "h")),
      ~ case_when(parameter == "b_time_to_abx" ~
                    exp(.x / tta.sd),
                  TRUE ~ exp(.x))
    )) %>%
    filter(str_detect(parameter, "abx") |
             str_detect(parameter, "_PC")) %>%
    mutate(type = "tta.model",
           adj = "adjusted"),
  mcmc_intervals_data(
    b.m.fluid.lin.univ,
    regex_pars = "^b_",
    prob_outer = 0.95
  ) %>%
    dplyr::mutate(across(
      starts_with(c("l", "m", "h")),
      ~ case_when(parameter == "b_fluid.6hr" ~
                    exp(.x / fluid.sd),
                  TRUE ~ exp(.x))
    )) %>%
    filter(str_detect(parameter, "fluid") |
             str_detect(parameter, "_PC")) %>%
    dplyr::mutate(type = "unadjusted",
                  adj = "unadjusted"),
  mcmc_intervals_data(b.m.fluid.lin,
                      regex_pars = "^b_",
                      prob_outer = 0.95) %>%
    dplyr::mutate(across(
      starts_with(c("l", "m", "h")),
      ~ case_when(parameter == "b_fluid.6hr" ~
                    exp(.x / fluid.sd),
                  TRUE ~ exp(.x))
    )) %>%
    filter(str_detect(parameter, "fluid") |
             str_detect(parameter, "_PC")) %>%
    mutate(type = "fluid.model",
           adj = "adjusted")
) ->
  df.plot


df.plot %>%
  mutate(parm_string = paste0(sp_dc(m, 2), " (", sp_dc(ll, 2),
                              "-", sp_dc(hh, 2), ")")) %>%
  mutate(parameter = str_replace(parameter, "t\\(b_|b_", "")) %>%
  mutate(
    parameter = str_replace(parameter, "\\)", "")
    ) %>% 
  select(parameter, parm_string, type, adj) %>%
  pivot_wider(
    id_cols = parameter,
    names_from = c(type, adj),
    values_from = parm_string
  ) -> mod.output.tab


mod.output.tab %>% 
  dplyr::mutate(
    parameter = dplyr::recode(
      parameter,
      tb1 = "Diagnosis is TB",
      tb.rx1 = "Received TB treatment",
      malaria1 = "Diagnosis is malaria",
      mal.rx1 = "Received malaria treatment",
      inv.fungal1 =
        "Diagnosis is invasive fungal disease",
      fung.rx1 = "Recieved antifungal",
      time_to_abx = "Time to antibacterial therapy (per hour)",
      fluid.6hr = "Vol of IV fluid (L)"
    )
  ) %>%
  dplyr::rename(
    "Unadjusted" = "unadjusted_unadjusted",
    "TB treatment" = "tb.model_adjusted",
    "Malaria treatment" = "mal.model_adjusted",
    "Fungal treatment" = "fung.model_adjusted",
    "Time to antibacterial" = "tta.model_adjusted",
    "Vol of IV fluid" = "fluid.model_adjusted"
  ) %>%
  dplyr::mutate(
    dplyr::across(
      dplyr::everything(),
      ~ if_else(is.na(.x), "-", .x)
      )
    ) -> mod.output.tab

kbl(mod.output.tab,
    row.names = F, caption = "SUPPLEMENTARY TABLE 8: Parameter estimates from models assessing effect of therapies on mortality, expressed as adjusted odds ratios with a point estimate (posterior median) and 95% credible intervals.") %>%
  kable_classic(full_width = FALSE)

SUPPLEMENTARY TABLE 8: Parameter estimates from models assessing effect of therapies on mortality, expressed as adjusted odds ratios with a point estimate (posterior median) and 95% credible intervals.
parameter	Unadjusted	TB treatment	Malaria treatment	Fungal treatment	Time to antibacterial	Vol of IV fluid
PC1	1.92 (1.43-2.66)	2.34 (1.73-3.31)	1.98 (1.51-2.71)	2.04 (1.53-2.83)	1.98 (1.51-2.71)	2.38 (1.75-3.39)
PC2	0.66 (0.49-0.89)	0.59 (0.44-0.78)	0.61 (0.46-0.79)	0.61 (0.46-0.80)	0.64 (0.48-0.83)	0.59 (0.44-0.77)
PC3	1.50 (1.07-2.18)	1.37 (1.02-1.89)	1.25 (0.92-1.74)	1.31 (0.97-1.81)	1.31 (0.97-1.81)	1.37 (1.02-1.89)
Diagnosis is TB	1.26 (0.60-2.57)	0.76 (0.30-1.87)
Received TB treatment	0.32 (0.10-0.84)	0.13 (0.03-0.42)
Diagnosis is malaria	0.04 (0.00-0.48)		0.07 (0.00-1.27)
Received malaria treatment	0.06 (0.00-0.84)		0.19 (0.00-5.50)
Diagnosis is invasive fungal disease	5.99 (1.01-42.19)			3.32 (0.41-32.01)
Recieved antifungal	1.81 (0.66-4.49)			0.79 (0.26-2.24)
Time to antibacterial therapy (per hour)	1.01 (0.98-1.03)				1.01 (0.99-1.04)
Vol of IV fluid (L)	1.20 (0.81-1.83)					0.53 (0.30-0.92)


#write_csv(mod.output.tab, "tables/SUP_mort_models_table.csv")

Final publication plot


# (1,2) PCA var plot and final mort plot  ------------------------------------


(p1  | p5 ) / (p.effects | p.tta | p.fluid) + 
  plot_annotation(tag_levels = "A") + 
  plot_layout(heights = c(2,1)) -> mort.model.plot.final

mort.model.plot.final

FIGURE 3: A: Host-severity principal components 1 and 2 showing that PC1 defines an axis of HIV, immunosuppression (low CD4 count and anaemia) and shock (tachycardia, low blood pressre and bicarbonate) whereas PC2 is associated with sepsis-related organ dysfunction, age, and male sex. B: Participants projected onto PC1 and 2 showing that participants who die (red circles) tend to have immunosuppression and shock (upper right), other sepsis related organ dysfunction (lower left), or both (lower right). C-E: Outputs of models predicting death by 28 days. Adjusted odds ratios and 95% credible intervals of effect of different antimicrobial therapies (C), predicted mortality as a function of time to antibacterial therapy (D) or volume of intravenous fluid received (E).

if (write_figs) {
  
ggsave( here("figures/MAIN_F3_mort_model.plot.pdf"),
        mort.model.plot.final, 
        width = 8, height = 8, units = "in")
ggsave( here("figures/MAIN_F3_mort_model.plot.tiff"),
        mort.model.plot.final, 
        width = 8, height = 8, units = "in",
        dpi = 600)
}

Reproducability


sessionInfo()
#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.6
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#> 
#> locale:
#> [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
#> 
#> attached base packages:
#> [1] splines   stats     graphics  grDevices utils     datasets  methods  
#> [8] base     
#> 
#> other attached packages:
#>  [1] here_0.1                  bayesplot_1.7.2          
#>  [3] mice_3.11.0               factoextra_1.0.7         
#>  [5] pheatmap_1.0.12           brms_2.13.5              
#>  [7] Rcpp_1.0.5                viridis_0.5.1            
#>  [9] viridisLite_0.3.0         wBoot_1.0.3              
#> [11] simpleboot_1.1-7          boot_1.3-25              
#> [13] survminer_0.4.8           ggpubr_0.4.0             
#> [15] ggplot2_3.3.3             patchwork_1.0.1          
#> [17] eulerr_6.1.0              UpSetR_1.4.0             
#> [19] ggplotify_0.0.5           kableExtra_1.1.0.9000    
#> [21] purrr_0.3.4               forcats_0.5.0            
#> [23] stringr_1.4.0             tidyr_1.1.1              
#> [25] dplyr_1.0.7               blantyreSepsis_1.0.0.0000
#> [27] survival_3.2-7           
#> 
#> loaded via a namespace (and not attached):
#>   [1] readxl_1.3.1         backports_1.1.8      systemfonts_0.3.2   
#>   [4] plyr_1.8.6           igraph_1.2.5         polylabelr_0.2.0    
#>   [7] listenv_0.8.0        crosstalk_1.1.0.1    rstantools_2.1.1    
#>  [10] inline_0.3.15        digest_0.6.26        htmltools_0.5.1.1   
#>  [13] rsconnect_0.8.16     fansi_0.4.1          checkmate_2.0.0     
#>  [16] magrittr_1.5         memoise_1.1.0        openxlsx_4.1.5      
#>  [19] globals_0.12.5       readr_1.3.1          RcppParallel_5.0.2  
#>  [22] matrixStats_0.56.0   xts_0.12-0           pkgdown_1.6.1       
#>  [25] prettyunits_1.1.1    colorspace_1.4-1     ggrepel_0.8.2       
#>  [28] blob_1.2.1           rvest_0.3.6          textshaping_0.1.2   
#>  [31] haven_2.3.1          xfun_0.16            jsonlite_1.7.1      
#>  [34] callr_3.5.1          crayon_1.3.4         zoo_1.8-8           
#>  [37] glue_1.4.2           polyclip_1.10-0      gtable_0.3.0        
#>  [40] webshot_0.5.2        V8_3.2.0             car_3.0-9           
#>  [43] pkgbuild_1.1.0       rstan_2.21.1         abind_1.4-5         
#>  [46] scales_1.1.1         mvtnorm_1.1-1        DBI_1.1.0           
#>  [49] rstatix_0.7.0        miniUI_0.1.1.1       xtable_1.8-4        
#>  [52] gridGraphics_0.5-0   foreign_0.8-80       km.ci_0.5-2         
#>  [55] splines2_0.3.1       StanHeaders_2.21.0-5 stats4_4.0.2        
#>  [58] DT_0.15              htmlwidgets_1.5.1    httr_1.4.2          
#>  [61] threejs_0.3.3        RColorBrewer_1.1-2   ellipsis_0.3.2      
#>  [64] farver_2.0.3         pkgconfig_2.0.3      loo_2.3.1           
#>  [67] utf8_1.1.4           labeling_0.3         tidyselect_1.1.0    
#>  [70] rlang_0.4.11         reshape2_1.4.4       later_1.1.0.1       
#>  [73] munsell_0.5.0        cellranger_1.1.0     tools_4.0.2         
#>  [76] cli_3.0.0            generics_0.0.2       broom_0.7.6         
#>  [79] ggridges_0.5.2       evaluate_0.14        fastmap_1.0.1       
#>  [82] yaml_2.2.1           ragg_0.4.0           processx_3.4.4      
#>  [85] knitr_1.29           fs_1.5.0             zip_2.1.0           
#>  [88] survMisc_0.5.5       future_1.18.0        nlme_3.1-148        
#>  [91] mime_0.9             xml2_1.3.2           compiler_4.0.2      
#>  [94] shinythemes_1.1.2    rstudioapi_0.11      curl_4.3            
#>  [97] ggsignif_0.6.0       tibble_3.0.3         stringi_1.4.6       
#> [100] highr_0.8            ps_1.3.4             desc_1.2.0          
#> [103] Brobdingnag_1.2-6    lattice_0.20-41      Matrix_1.2-18       
#> [106] markdown_1.1         shinyjs_1.1          KMsurv_0.1-5        
#> [109] vctrs_0.3.8          pillar_1.6.1         lifecycle_1.0.0     
#> [112] BiocManager_1.30.10  bridgesampling_1.0-0 data.table_1.13.0   
#> [115] httpuv_1.5.4         R6_2.4.1             promises_1.1.1      
#> [118] gridExtra_2.3        rio_0.5.16           codetools_0.2-16    
#> [121] colourpicker_1.0     gtools_3.8.2         assertthat_0.2.1    
#> [124] rprojroot_1.3-2      withr_2.2.0          shinystan_2.5.0     
#> [127] parallel_4.0.2       hms_0.5.3            grid_4.0.2          
#> [130] coda_0.19-3          rmarkdown_2.6        rvcheck_0.1.8       
#> [133] carData_3.0-4        shiny_1.5.0          base64enc_0.1-3     
#> [136] dygraphs_1.1.1.6

Joe Lewis

23 August, 2021