TLG Catalog - Stable
  • Stable
    • Dev
  1. Tables
  2. Deaths
  3. DTHT01
  • Introduction

  • Tables
    • ADA
      • ADAT01
      • ADAT02
      • ADAT03
      • ADAT04A
      • ADAT04B
    • Adverse Events
      • AET01
      • AET01_AESI
      • AET02
      • AET02_SMQ
      • AET03
      • AET04
      • AET04_PI
      • AET05
      • AET05_ALL
      • AET06
      • AET06_SMQ
      • AET07
      • AET09
      • AET09_SMQ
      • AET10
    • Concomitant Medications
      • CMT01
      • CMT01A
      • CMT01B
      • CMT02_PT
    • Deaths
      • DTHT01
    • Demography
      • DMT01
    • Disclosures
      • DISCLOSUREST01
      • EUDRAT01
      • EUDRAT02
    • Disposition
      • DST01
      • PDT01
      • PDT02
    • ECG
      • EGT01
      • EGT02
      • EGT03
      • EGT04
      • EGT05_QTCAT
    • Efficacy
      • AOVT01
      • AOVT02
      • AOVT03
      • CFBT01
      • CMHT01
      • COXT01
      • COXT02
      • DORT01
      • LGRT02
      • MMRMT01
      • ONCT05
      • RATET01
      • RBMIT01
      • RSPT01
      • TTET01
    • Exposure
      • EXT01
    • Lab Results
      • LBT01
      • LBT02
      • LBT03
      • LBT04
      • LBT05
      • LBT06
      • LBT07
      • LBT08
      • LBT09
      • LBT10
      • LBT10_BL
      • LBT11
      • LBT11_BL
      • LBT12
      • LBT12_BL
      • LBT13
      • LBT14
      • LBT15
    • Medical History
      • MHT01
    • Pharmacokinetic
      • PKCT01
      • PKPT02
      • PKPT03
      • PKPT04
      • PKPT05
      • PKPT06
      • PKPT07
      • PKPT08
      • PKPT11
    • Risk Management Plan
      • RMPT01
      • RMPT03
      • RMPT04
      • RMPT05
      • RMPT06
    • Safety
      • ENTXX
    • Vital Signs
      • VST01
      • VST02
  • Listings
    • ADA
      • ADAL02
    • Adverse Events
      • AEL01
      • AEL01_NOLLT
      • AEL02
      • AEL02_ED
      • AEL03
      • AEL04
    • Concomitant Medications
      • CML01
      • CML02A_GL
      • CML02B_GL
    • Development Safety Update Report
      • DSUR4
    • Disposition
      • DSL01
      • DSL02
    • ECG
      • EGL01
    • Efficacy
      • ONCL01
    • Exposure
      • EXL01
    • Lab Results
      • LBL01
      • LBL01_RLS
      • LBL02A
      • LBL02A_RLS
      • LBL02B
    • Medical History
      • MHL01
    • Pharmacokinetic
      • ADAL01
      • PKCL01
      • PKCL02
      • PKPL01
      • PKPL02
      • PKPL04
    • Vital Signs
      • VSL01
  • Graphs
    • Efficacy
      • FSTG01
      • FSTG02
      • KMG01
      • MMRMG01
      • MMRMG02
    • Other
      • BRG01
      • BWG01
      • CIG01
      • IPPG01
      • LTG01
      • MNG01
    • Pharmacokinetic
      • PKCG01
      • PKCG02
      • PKCG03
      • PKPG01
      • PKPG02
      • PKPG03
      • PKPG04
      • PKPG06

  • Appendix
    • Reproducibility

  • Index

On this page

  • Output
  • Reproducibility
    • Timestamp
    • Session Info
    • .lock file
  • Edit this page
  • Report an issue
  1. Tables
  2. Deaths
  3. DTHT01

DTHT01

Deaths


Output

  • Standard Table
  • Table Selecting
    Sections to Display
  • Table for Studies Collecting Death
    Information from Public Records
  • Table Adding Details for “All other causes”
    Category for Studies Collecting Death
    Information from Public Records
  • Data Setup
  • Preview
  • Try this using WebR
Code
lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", split_fun = add_overall_level("All Patients", first = FALSE)) %>%
  count_values(
    "DTHFL",
    values = "Y",
    .labels = c(count_fraction = "Total number of deaths"),
    .formats = c(count_fraction = "xx (xx.x%)")
  ) %>%
  analyze_vars(vars = c("DTHCAT"), var_labels = c("Primary Cause of Death"))

result <- build_table(lyt, df = adsl)
result
                         A: Drug X    B: Placebo   C: Combination   All Patients
                          (N=134)      (N=134)        (N=132)         (N=400)   
————————————————————————————————————————————————————————————————————————————————
Total number of deaths   25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) 
Primary Cause of Death                                                          
  n                          25           23             22              70     
  ADVERSE EVENT           9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) 
  PROGRESSIVE DISEASE     8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) 
  OTHER                   8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) 
Experimental use!

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Code
lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", split_fun = add_overall_level("All Patients", first = FALSE)) %>%
  count_values(
    "DTHFL",
    values = "Y",
    .labels = c(count_fraction = "Total number of deaths"),
    .formats = c(count_fraction = "xx (xx.x%)")
  ) %>%
  analyze_vars(vars = c("DTHCAT"), var_labels = c("Primary Cause of Death")) %>%
  split_rows_by("DTHCAT", split_fun = keep_split_levels("OTHER"), child_labels = "hidden") %>%
  analyze_vars(
    "DTHCAUS",
    .stats = "count_fraction",
    .indent_mods = c("count_fraction" = 2L),
    show_labels = "hidden"
  ) %>%
  analyze_vars(
    vars = "LDDTHGR1",
    nested = FALSE,
    var_labels = "Days from last drug administration",
    show_labels = "visible"
  ) %>%
  split_rows_by(
    "LDDTHGR1",
    split_fun = remove_split_levels("<Missing>"),
    split_label = "Primary cause by days from last study drug administration",
    label_pos = "visible"
  ) %>%
  analyze_vars("DTHCAT")

result <- build_table(lyt, df = adsl) %>% prune_table()
Modifying subtable (or row) names to ensure uniqueness among direct siblings
[LDDTHGR1  -> { LDDTHGR1, LDDTHGR1[2] }]
  To control table names use split_rows_by*(, parent_name =.) or  analyze(., table_names = .) when analyzing a single variable, or analyze(., parent_name = .) when analyzing multiple variables in a single call.FALSE
Code
result
                                                            A: Drug X    B: Placebo   C: Combination   All Patients
                                                             (N=134)      (N=134)        (N=132)         (N=400)   
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of deaths                                      25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) 
Primary Cause of Death                                                                                             
  n                                                             25           23             22              70     
  ADVERSE EVENT                                              9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) 
  PROGRESSIVE DISEASE                                        8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) 
  OTHER                                                      8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) 
    LOST TO FOLLOW UP                                        2 (25%)      2 (20%)       2 (33.3%)        6 (25%)   
    MISSING                                                  2 (25%)      3 (30%)       2 (33.3%)       7 (29.2%)  
    Post-study reporting of death                           1 (12.5%)     2 (20%)       1 (16.7%)       4 (16.7%)  
    SUICIDE                                                  2 (25%)      2 (20%)       1 (16.7%)       5 (20.8%)  
    UNKNOWN                                                 1 (12.5%)     1 (10%)           0            2 (8.3%)  
Days from last drug administration                                                                                 
  n                                                             25           23             22              70     
  <=30                                                       14 (56%)    11 (47.8%)     14 (63.6%)      39 (55.7%) 
  >30                                                        11 (44%)    12 (52.2%)     8 (36.4%)       31 (44.3%) 
Primary cause by days from last study drug administration                                                          
  <=30                                                                                                             
    n                                                           14           11             14              39     
    ADVERSE EVENT                                           4 (28.6%)    2 (18.2%)      6 (42.9%)       12 (30.8%) 
    PROGRESSIVE DISEASE                                     6 (42.9%)    3 (27.3%)      4 (28.6%)       13 (33.3%) 
    OTHER                                                   4 (28.6%)    6 (54.5%)      4 (28.6%)       14 (35.9%) 
  >30                                                                                                              
    n                                                           11           12             8               31     
    ADVERSE EVENT                                           5 (45.5%)    5 (41.7%)       4 (50%)        14 (45.2%) 
    PROGRESSIVE DISEASE                                     2 (18.2%)     3 (25%)        2 (25%)        7 (22.6%)  
    OTHER                                                   4 (36.4%)    4 (33.3%)       2 (25%)        10 (32.3%) 
Experimental use!

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Code
dthcaus_levels <- levels(adsl[adsl$DTHCAT == "OTHER", ]$DTHCAUS)

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", split_fun = add_overall_level("All Patients", first = FALSE)) %>%
  count_values(
    "DTHFL",
    values = "Y",
    .labels = c(count_fraction = "Total number of deaths"),
    .formats = c(count_fraction = "xx (xx.x%)")
  ) %>%
  analyze_vars(
    vars = c("DTHCAT"),
    var_labels = c("Primary Cause of Death"),
    table_names = "primary_cause"
  ) %>%
  split_rows_by(
    "DTHCAT",
    split_fun = keep_split_levels("OTHER"),
    child_labels = "hidden"
  ) %>%
  count_values(
    "DTHCAUS",
    values = dthcaus_levels[5],
    .labels = c(count_fraction = "Post-study reportings of death"),
    .formats = c(count_fraction = "xx (xx.x%)"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "post_study_deaths"
  ) %>%
  count_values(
    "DTHCAUS",
    values = dthcaus_levels[-5],
    .labels = c(count_fraction = "All other causes"),
    .formats = c(count_fraction = "xx (xx.x%)"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "all_other_causes"
  )

result <- build_table(lyt, df = adsl)

result
                                     A: Drug X    B: Placebo   C: Combination   All Patients
                                      (N=134)      (N=134)        (N=132)         (N=400)   
————————————————————————————————————————————————————————————————————————————————————————————
Total number of deaths               25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) 
Primary Cause of Death                                                                      
  n                                      25           23             22              70     
  ADVERSE EVENT                       9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) 
  PROGRESSIVE DISEASE                 8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) 
  OTHER                               8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) 
    Post-study reportings of death   1 (12.5%)    2 (20.0%)      1 (16.7%)       4 (16.7%)  
    All other causes                 7 (87.5%)    8 (80.0%)      5 (83.3%)       20 (83.3%) 
Experimental use!

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Code
dthcaus_levels <- levels(adsl[adsl$DTHCAT == "OTHER", ]$DTHCAUS)

# create a helper variable DTHCAUS_other
adsl <- adsl %>%
  mutate(
    DTHCAUS_other = factor(ifelse(
      DTHCAT == "OTHER" & DTHCAUS != "Post-study reporting of death", as.character(DTHCAUS), NA
    ), levels = c("LOST TO FOLLOW UP", "SUICIDE", "UNKNOWN", "MISSING")) %>% explicit_na()
  )

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", split_fun = add_overall_level("All Patients", first = FALSE)) %>%
  count_values(
    "DTHFL",
    values = "Y",
    .labels = c(count_fraction = "Total number of deaths"),
    .formats = c(count_fraction = "xx (xx.x%)")
  ) %>%
  analyze_vars(
    vars = c("DTHCAT"),
    var_labels = c("Primary Cause of Death"),
    table_names = "primary_cause"
  ) %>%
  split_rows_by("DTHCAT", split_fun = keep_split_levels("OTHER"), child_labels = "hidden") %>%
  count_values(
    "DTHCAUS",
    values = dthcaus_levels[5],
    .labels = c(count_fraction = "Post-study reportings of death"),
    .formats = c(count_fraction = "xx (xx.x%)"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "post_study_deaths"
  ) %>%
  count_values(
    "DTHCAUS",
    values = dthcaus_levels[-5],
    .labels = c(count_fraction = "All other causes"),
    .formats = c(count_fraction = "xx (xx.x%)"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "all_other_causes"
  ) %>%
  analyze_vars(
    "DTHCAUS_other",
    .stats = "count_fraction",
    .indent_mods = c("count_fraction" = 3L),
    show_labels = "hidden"
  )

result <- build_table(lyt, df = adsl)

result
                                     A: Drug X    B: Placebo   C: Combination   All Patients
                                      (N=134)      (N=134)        (N=132)         (N=400)   
————————————————————————————————————————————————————————————————————————————————————————————
Total number of deaths               25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) 
Primary Cause of Death                                                                      
  n                                      25           23             22              70     
  ADVERSE EVENT                       9 (36%)     7 (30.4%)      10 (45.5%)      26 (37.1%) 
  PROGRESSIVE DISEASE                 8 (32%)     6 (26.1%)      6 (27.3%)       20 (28.6%) 
  OTHER                               8 (32%)     10 (43.5%)     6 (27.3%)       24 (34.3%) 
    Post-study reportings of death   1 (12.5%)    2 (20.0%)      1 (16.7%)       4 (16.7%)  
    All other causes                 7 (87.5%)    8 (80.0%)      5 (83.3%)       20 (83.3%) 
      LOST TO FOLLOW UP              2 (28.6%)     2 (25%)        2 (40%)         6 (30%)   
      SUICIDE                        2 (28.6%)     2 (25%)        1 (20%)         5 (25%)   
      UNKNOWN                        1 (14.3%)    1 (12.5%)          0            2 (10%)   
      MISSING                        2 (28.6%)    3 (37.5%)       2 (40%)         7 (35%)   
Experimental use!

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Code
library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl) %>% filter(SAFFL == "Y")

# Reorder the levels in "DTHCAT" to put Other category at the end.
adsl$DTHCAT <- factor(adsl$DTHCAT, levels = c("ADVERSE EVENT", "PROGRESSIVE DISEASE", "OTHER", "<Missing>"))

Reproducibility

Timestamp

[1] "2025-07-05 17:51:28 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11)
 os       Ubuntu 24.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-07-05
 pandoc   3.7.0.2 @ /usr/bin/ (via rmarkdown)
 quarto   1.7.32 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package           * version date (UTC) lib source
 backports           1.5.0   2024-05-23 [1] RSPM
 brio                1.1.5   2024-04-24 [1] RSPM
 broom               1.0.8   2025-03-28 [1] RSPM
 checkmate           2.3.2   2024-07-29 [1] RSPM
 cli                 3.6.5   2025-04-23 [1] RSPM
 codetools           0.2-20  2024-03-31 [2] CRAN (R 4.5.0)
 curl                6.4.0   2025-06-22 [1] RSPM
 dichromat           2.0-0.1 2022-05-02 [1] CRAN (R 4.5.0)
 digest              0.6.37  2024-08-19 [1] RSPM
 dplyr             * 1.1.4   2023-11-17 [1] RSPM
 evaluate            1.0.4   2025-06-18 [1] RSPM
 farver              2.1.2   2024-05-13 [1] RSPM
 fastmap             1.2.0   2024-05-15 [1] RSPM
 forcats             1.0.0   2023-01-29 [1] RSPM
 formatters        * 0.5.11  2025-04-09 [1] RSPM
 generics            0.1.4   2025-05-09 [1] RSPM
 ggplot2             3.5.2   2025-04-09 [1] RSPM
 glue                1.8.0   2024-09-30 [1] RSPM
 gtable              0.3.6   2024-10-25 [1] RSPM
 htmltools           0.5.8.1 2024-04-04 [1] RSPM
 htmlwidgets         1.6.4   2023-12-06 [1] RSPM
 jsonlite            2.0.0   2025-03-27 [1] RSPM
 knitr               1.50    2025-03-16 [1] RSPM
 lattice             0.22-7  2025-04-02 [2] CRAN (R 4.5.0)
 lifecycle           1.0.4   2023-11-07 [1] RSPM
 magrittr          * 2.0.3   2022-03-30 [1] RSPM
 Matrix              1.7-3   2025-03-11 [1] CRAN (R 4.5.0)
 nestcolor           0.1.3   2025-01-21 [1] RSPM
 pillar              1.11.0  2025-07-04 [1] RSPM
 pkgcache            2.2.4   2025-05-26 [1] RSPM
 pkgconfig           2.0.3   2019-09-22 [1] RSPM
 processx            3.8.6   2025-02-21 [1] RSPM
 ps                  1.9.1   2025-04-12 [1] RSPM
 purrr               1.0.4   2025-02-05 [1] RSPM
 R6                  2.6.1   2025-02-15 [1] RSPM
 random.cdisc.data   0.3.16  2024-10-10 [1] RSPM
 rbibutils           2.3     2024-10-04 [1] RSPM
 RColorBrewer        1.1-3   2022-04-03 [1] RSPM
 Rdpack              2.6.4   2025-04-09 [1] RSPM
 rlang               1.1.6   2025-04-11 [1] RSPM
 rmarkdown           2.29    2024-11-04 [1] RSPM
 rtables           * 0.6.13  2025-06-19 [1] RSPM
 scales              1.4.0   2025-04-24 [1] RSPM
 sessioninfo         1.2.3   2025-02-05 [1] any (@1.2.3)
 stringi             1.8.7   2025-03-27 [1] RSPM
 stringr             1.5.1   2023-11-14 [1] RSPM
 survival            3.8-3   2024-12-17 [2] CRAN (R 4.5.0)
 tern              * 0.9.9   2025-06-20 [1] RSPM
 testthat            3.2.3   2025-01-13 [1] RSPM
 tibble              3.3.0   2025-06-08 [1] RSPM
 tidyr               1.3.1   2024-01-24 [1] RSPM
 tidyselect          1.2.1   2024-03-11 [1] RSPM
 vctrs               0.6.5   2023-12-01 [1] RSPM
 xfun                0.52    2025-04-02 [1] RSPM
 yaml                2.3.10  2024-07-26 [1] RSPM

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library
 [3] /github/home/R/x86_64-pc-linux-gnu-library/4.5
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

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Download the .lock file and use renv::restore() on it to recreate environment used to generate this website.

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CMT02_PT
DMT01
Source Code
---
title: DTHT01
subtitle: Deaths
---

------------------------------------------------------------------------

{{< include ../../_utils/envir_hook.qmd >}}

```{r setup, echo = FALSE, warning = FALSE, message = FALSE}
library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl) %>% filter(SAFFL == "Y")

# Reorder the levels in "DTHCAT" to put Other category at the end.
adsl$DTHCAT <- factor(adsl$DTHCAT, levels = c("ADVERSE EVENT", "PROGRESSIVE DISEASE", "OTHER", "<Missing>"))
```

```{r include = FALSE}
webr_code_labels <- c("setup")
```

{{< include ../../_utils/webr_no_include.qmd >}}

## Output

::::::: panel-tabset
## Standard Table

::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview

```{r variant1, test = list(result_v1 = "result")}
lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", split_fun = add_overall_level("All Patients", first = FALSE)) %>%
  count_values(
    "DTHFL",
    values = "Y",
    .labels = c(count_fraction = "Total number of deaths"),
    .formats = c(count_fraction = "xx (xx.x%)")
  ) %>%
  analyze_vars(vars = c("DTHCAT"), var_labels = c("Primary Cause of Death"))

result <- build_table(lyt, df = adsl)
result
```

```{r include = FALSE}
webr_code_labels <- c("variant1")
```

{{< include ../../_utils/webr.qmd >}}
:::

## Table Selecting <br/> Sections to Display

::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview

```{r variant2, test = list(result_v2 = "result")}
lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", split_fun = add_overall_level("All Patients", first = FALSE)) %>%
  count_values(
    "DTHFL",
    values = "Y",
    .labels = c(count_fraction = "Total number of deaths"),
    .formats = c(count_fraction = "xx (xx.x%)")
  ) %>%
  analyze_vars(vars = c("DTHCAT"), var_labels = c("Primary Cause of Death")) %>%
  split_rows_by("DTHCAT", split_fun = keep_split_levels("OTHER"), child_labels = "hidden") %>%
  analyze_vars(
    "DTHCAUS",
    .stats = "count_fraction",
    .indent_mods = c("count_fraction" = 2L),
    show_labels = "hidden"
  ) %>%
  analyze_vars(
    vars = "LDDTHGR1",
    nested = FALSE,
    var_labels = "Days from last drug administration",
    show_labels = "visible"
  ) %>%
  split_rows_by(
    "LDDTHGR1",
    split_fun = remove_split_levels("<Missing>"),
    split_label = "Primary cause by days from last study drug administration",
    label_pos = "visible"
  ) %>%
  analyze_vars("DTHCAT")

result <- build_table(lyt, df = adsl) %>% prune_table()

result
```

```{r include = FALSE}
webr_code_labels <- c("variant2")
```

{{< include ../../_utils/webr.qmd >}}
:::

## Table for Studies Collecting Death <br/> Information from Public Records

::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview

```{r variant3, test = list(result_v3 = "result")}
dthcaus_levels <- levels(adsl[adsl$DTHCAT == "OTHER", ]$DTHCAUS)

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", split_fun = add_overall_level("All Patients", first = FALSE)) %>%
  count_values(
    "DTHFL",
    values = "Y",
    .labels = c(count_fraction = "Total number of deaths"),
    .formats = c(count_fraction = "xx (xx.x%)")
  ) %>%
  analyze_vars(
    vars = c("DTHCAT"),
    var_labels = c("Primary Cause of Death"),
    table_names = "primary_cause"
  ) %>%
  split_rows_by(
    "DTHCAT",
    split_fun = keep_split_levels("OTHER"),
    child_labels = "hidden"
  ) %>%
  count_values(
    "DTHCAUS",
    values = dthcaus_levels[5],
    .labels = c(count_fraction = "Post-study reportings of death"),
    .formats = c(count_fraction = "xx (xx.x%)"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "post_study_deaths"
  ) %>%
  count_values(
    "DTHCAUS",
    values = dthcaus_levels[-5],
    .labels = c(count_fraction = "All other causes"),
    .formats = c(count_fraction = "xx (xx.x%)"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "all_other_causes"
  )

result <- build_table(lyt, df = adsl)

result
```

```{r include = FALSE}
webr_code_labels <- c("variant3")
```

{{< include ../../_utils/webr.qmd >}}
:::

## Table Adding Details for "All other causes" <br/> Category for Studies Collecting Death <br/> Information from Public Records

::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview

```{r variant4, test = list(result_v4 = "result")}
dthcaus_levels <- levels(adsl[adsl$DTHCAT == "OTHER", ]$DTHCAUS)

# create a helper variable DTHCAUS_other
adsl <- adsl %>%
  mutate(
    DTHCAUS_other = factor(ifelse(
      DTHCAT == "OTHER" & DTHCAUS != "Post-study reporting of death", as.character(DTHCAUS), NA
    ), levels = c("LOST TO FOLLOW UP", "SUICIDE", "UNKNOWN", "MISSING")) %>% explicit_na()
  )

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM", split_fun = add_overall_level("All Patients", first = FALSE)) %>%
  count_values(
    "DTHFL",
    values = "Y",
    .labels = c(count_fraction = "Total number of deaths"),
    .formats = c(count_fraction = "xx (xx.x%)")
  ) %>%
  analyze_vars(
    vars = c("DTHCAT"),
    var_labels = c("Primary Cause of Death"),
    table_names = "primary_cause"
  ) %>%
  split_rows_by("DTHCAT", split_fun = keep_split_levels("OTHER"), child_labels = "hidden") %>%
  count_values(
    "DTHCAUS",
    values = dthcaus_levels[5],
    .labels = c(count_fraction = "Post-study reportings of death"),
    .formats = c(count_fraction = "xx (xx.x%)"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "post_study_deaths"
  ) %>%
  count_values(
    "DTHCAUS",
    values = dthcaus_levels[-5],
    .labels = c(count_fraction = "All other causes"),
    .formats = c(count_fraction = "xx (xx.x%)"),
    .indent_mods = c(count_fraction = 2L),
    table_names = "all_other_causes"
  ) %>%
  analyze_vars(
    "DTHCAUS_other",
    .stats = "count_fraction",
    .indent_mods = c("count_fraction" = 3L),
    show_labels = "hidden"
  )

result <- build_table(lyt, df = adsl)

result
```

```{r include = FALSE}
webr_code_labels <- c("variant4")
```

{{< include ../../_utils/webr.qmd >}}
:::

## Data Setup

```{r setup}
#| code-fold: show
```
:::::::

{{< include ../../_utils/save_results.qmd >}}

{{< include ../../repro.qmd >}}

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