DST01

Patient Disposition

Preview
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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_occurrences(
    "EOSSTT",
    show_labels = "hidden"
  ) %>%
  analyze_vars(
    "DCSREAS",
    .stats = "count_fraction",
    denom = "N_col",
    show_labels = "hidden",
    .indent_mods = c(count_fraction = 1L)
  )

result1 <- build_table(lyt = lyt, df = adsl)
result1

                                  A: Drug X    B: Placebo   C: Combination   All Patients
                                   (N=134)      (N=134)        (N=132)         (N=400)   
—————————————————————————————————————————————————————————————————————————————————————————
COMPLETED                         68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) 
ONGOING                           24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) 
DISCONTINUED                      42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) 
  ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  
  DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) 
  LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  
  PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  
  PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   
  WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  
  WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

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_occurrences(
    "EOSSTT",
    show_labels = "hidden"
  ) %>%
  split_rows_by("DCSREASGP", indent_mod = 1L) %>%
  analyze_vars(
    "DCSREAS",
    .stats = "count_fraction",
    denom = "N_col",
    show_labels = "hidden"
  )

tbl <- build_table(lyt = lyt, df = adsl_gp_added)
result2 <- prune_table(tbl) # remove rows containing all zeros

result2

                                    A: Drug X    B: Placebo   C: Combination   All Patients
                                     (N=134)      (N=134)        (N=132)         (N=400)   
———————————————————————————————————————————————————————————————————————————————————————————
COMPLETED                           68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) 
ONGOING                             24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) 
DISCONTINUED                        42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) 
  Safety                                                                                   
    ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  
    DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) 
  Non-Safety                                                                               
    LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  
    PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  
    PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   
    WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  
    WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

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_occurrences(
    "EOTSTT",
    show_labels = "hidden"
  )

tbl <- build_table(lyt = lyt, df = adsl_eotstt_added)
tbl <- prune_table(tbl) # remove rows containing all zeros

# Combine tables
col_info(result2) <- col_info(tbl)
result3 <- rbind(result2, tbl)

result3

                                    A: Drug X    B: Placebo   C: Combination   All Patients
                                     (N=134)      (N=134)        (N=132)         (N=400)   
———————————————————————————————————————————————————————————————————————————————————————————
COMPLETED                           68 (50.7%)   66 (49.3%)     73 (55.3%)     207 (51.7%) 
ONGOING                             24 (17.9%)   28 (20.9%)     21 (15.9%)      73 (18.2%) 
DISCONTINUED                        42 (31.3%)   40 (29.9%)     38 (28.8%)     120 (30.0%) 
  Safety                                                                                   
    ADVERSE EVENT                    3 (2.2%)     6 (4.5%)       5 (3.8%)       14 (3.5%)  
    DEATH                           25 (18.7%)   23 (17.2%)     22 (16.7%)      70 (17.5%) 
  Non-Safety                                                                               
    LACK OF EFFICACY                 2 (1.5%)     2 (1.5%)       3 (2.3%)        7 (1.8%)  
    PHYSICIAN DECISION               2 (1.5%)     3 (2.2%)       2 (1.5%)        7 (1.8%)  
    PROTOCOL VIOLATION               5 (3.7%)     3 (2.2%)        4 (3%)         12 (3%)   
    WITHDRAWAL BY PARENT/GUARDIAN     4 (3%)      2 (1.5%)       1 (0.8%)        7 (1.8%)  
    WITHDRAWAL BY SUBJECT            1 (0.7%)     1 (0.7%)       1 (0.8%)        3 (0.8%)  
COMPLETED                           46 (34.3%)   38 (28.4%)     41 (31.1%)     125 (31.2%) 
ONGOING                             50 (37.3%)   51 (38.1%)     46 (34.8%)     147 (36.8%) 
DISCONTINUED                        38 (28.4%)   45 (33.6%)     45 (34.1%)     128 (32.0%)

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Code

library(tern)
library(dplyr)

set.seed(1, kind = "Mersenne-Twister")
adsl <- random.cdisc.data::cadsl

# reorder EOSSTT factor levels so DISCONTINUED is the last level
adsl <- df_explicit_na(adsl) %>%
  mutate(EOSSTT = factor(EOSSTT, levels = c("COMPLETED", "ONGOING", "DISCONTINUED")))

adsl_gp_added <- adsl %>%
  mutate(DCSREASGP = case_when(
    DCSREAS %in% c("ADVERSE EVENT", "DEATH") ~ "Safety",
    (DCSREAS != "<Missing>" & !DCSREAS %in% c("ADVERSE EVENT", "DEATH")) ~ "Non-Safety",
    DCSREAS == "<Missing>" ~ "<Missing>"
  ) %>% factor(levels = c("Safety", "Non-Safety", "<Missing>")))

adsl_eotstt_added <- adsl_gp_added %>%
  mutate(
    EOTSTT = sample(
      c("ONGOING", "COMPLETED", "DISCONTINUED"),
      size = nrow(adsl),
      replace = TRUE
    ) %>% factor(levels = c("COMPLETED", "ONGOING", "DISCONTINUED"))
  )

Code

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  set.seed(1, kind = "Mersenne-Twister")
  ADSL <- random.cdisc.data::cadsl
  ADSL <- df_explicit_na(ADSL)
  ADSL <- ADSL %>%
    mutate(
      DCSREASGP = case_when(
        DCSREAS %in% c("ADVERSE EVENT", "DEATH") ~ "Safety",
        (DCSREAS != "<Missing>" & !DCSREAS %in% c("ADVERSE EVENT", "DEATH")) ~ "Non-Safety",
        DCSREAS == "<Missing>" ~ "<Missing>"
      ) %>% as.factor(),
      EOTSTT = sample(
        c("ONGOING", "COMPLETED", "DISCONTINUED"),
        size = nrow(ADSL),
        replace = TRUE
      ) %>% as.factor()
    ) %>%
    col_relabel(
      EOTSTT = "End Of Treatment Status"
    )

  date_vars_asl <- names(ADSL)[vapply(ADSL, function(x) inherits(x, c("Date", "POSIXct", "POSIXlt")), logical(1))]
  demog_vars_asl <- names(ADSL)[!(names(ADSL) %in% c("USUBJID", "STUDYID", date_vars_asl))]
})
datanames <- "ADSL"
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
demog_vars_asl <- data[["demog_vars_asl"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary(
      label = "Disposition Table",
      dataname = "ADSL",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      summarize_vars = choices_selected(
        variable_choices(ADSL, demog_vars_asl),
        c("EOSSTT", "DCSREAS", "EOTSTT")
      ),
      useNA = "ifany"
    )
  )
)

shinyApp(app$ui, app$server)

Experimental use!

shinylive allow you to modify to run shiny application entirely in the web browser. Modify the code below and click re-run the app to see the results. The performance is slighly worse and some of the features (e.g. downloading) might not work at all.

#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://insightsengineering.r-universe.dev", getOption("webr_pkg_repos")))

# -- APP CODE --
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  set.seed(1, kind = "Mersenne-Twister")
  ADSL <- random.cdisc.data::cadsl
  ADSL <- df_explicit_na(ADSL)
  ADSL <- ADSL %>%
    mutate(
      DCSREASGP = case_when(
        DCSREAS %in% c("ADVERSE EVENT", "DEATH") ~ "Safety",
        (DCSREAS != "<Missing>" & !DCSREAS %in% c("ADVERSE EVENT", "DEATH")) ~ "Non-Safety",
        DCSREAS == "<Missing>" ~ "<Missing>"
      ) %>% as.factor(),
      EOTSTT = sample(
        c("ONGOING", "COMPLETED", "DISCONTINUED"),
        size = nrow(ADSL),
        replace = TRUE
      ) %>% as.factor()
    ) %>%
    col_relabel(
      EOTSTT = "End Of Treatment Status"
    )

  date_vars_asl <- names(ADSL)[vapply(ADSL, function(x) inherits(x, c("Date", "POSIXct", "POSIXlt")), logical(1))]
  demog_vars_asl <- names(ADSL)[!(names(ADSL) %in% c("USUBJID", "STUDYID", date_vars_asl))]
})
datanames <- "ADSL"
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
demog_vars_asl <- data[["demog_vars_asl"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary(
      label = "Disposition Table",
      dataname = "ADSL",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      summarize_vars = choices_selected(
        variable_choices(ADSL, demog_vars_asl),
        c("EOSSTT", "DCSREAS", "EOTSTT")
      ),
      useNA = "ifany"
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:33:56 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-02
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
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 bslib                   0.8.0    2024-07-29 [1] CRAN (R 4.4.1)
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 estimability            1.5.1    2024-05-12 [1] CRAN (R 4.4.1)
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 forcats                 1.0.0    2023-01-29 [1] CRAN (R 4.4.1)
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 jsonlite                1.8.9    2024-09-20 [1] CRAN (R 4.4.1)
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 Matrix                  1.7-0    2024-04-26 [1] CRAN (R 4.4.1)
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 mvtnorm                 1.3-1    2024-09-03 [1] CRAN (R 4.4.1)
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 purrr                   1.0.2    2023-08-10 [1] CRAN (R 4.4.1)
 R6                      2.5.1    2021-08-19 [1] CRAN (R 4.4.1)
 random.cdisc.data       0.3.16   2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
 rbibutils               2.3      2024-10-04 [1] CRAN (R 4.4.1)
 Rcpp                    1.0.13   2024-07-17 [1] CRAN (R 4.4.1)
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 rlang                   1.1.4    2024-06-04 [1] CRAN (R 4.4.1)
 rmarkdown               2.28     2024-08-17 [1] CRAN (R 4.4.1)
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 scales                  1.3.0    2023-11-28 [1] CRAN (R 4.4.1)
 sessioninfo             1.2.2    2021-12-06 [1] CRAN (R 4.4.1)
 shiny                 * 1.9.1    2024-08-01 [1] CRAN (R 4.4.1)
 shinycssloaders         1.1.0    2024-07-30 [1] CRAN (R 4.4.1)
 shinyjs                 2.1.0    2021-12-23 [1] CRAN (R 4.4.1)
 shinyvalidate           0.1.3    2023-10-04 [1] CRAN (R 4.4.1)
 shinyWidgets            0.8.7    2024-09-23 [1] CRAN (R 4.4.1)
 stringi                 1.8.4    2024-05-06 [1] CRAN (R 4.4.1)
 stringr                 1.5.1    2023-11-14 [1] CRAN (R 4.4.1)
 survival                3.7-0    2024-06-05 [2] CRAN (R 4.4.1)
 teal                  * 0.15.2   2024-03-07 [1] CRAN (R 4.4.1)
 teal.code             * 0.5.0    2024-01-11 [1] CRAN (R 4.4.1)
 teal.data             * 0.6.0    2024-04-30 [1] CRAN (R 4.4.1)
 teal.logger             0.3.0    2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
 teal.modules.clinical * 0.9.1    2024-04-27 [1] CRAN (R 4.4.1)
 teal.reporter           0.3.1    2024-03-15 [1] CRAN (R 4.4.1)
 teal.slice            * 0.5.1    2024-04-30 [1] CRAN (R 4.4.1)
 teal.transform        * 0.5.0    2024-02-17 [1] CRAN (R 4.4.1)
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

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Reproducibility

Timestamp

Session Info

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