EXT01

Study Drug Exposure Table

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Code

# When summary table contains only categorical or only numeric parameters

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  split_rows_by("PARAM", split_fun = split_fun) %>%
  analyze_vars(vars = "AVAL")

result <- build_table(lyt = lyt, df = adex, alt_counts_df = adsl)

result

                                                 A: Drug X        B: Placebo      C: Combination 
Parameter Category (Drug A/Drug B)                (N=134)           (N=134)           (N=132)    
—————————————————————————————————————————————————————————————————————————————————————————————————
Drug A                                                                                           
  Overall duration (days)                                                                        
    n                                               75                67                75       
    Mean (SD)                                   74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  
    Median                                         77.0              80.0              78.0      
    Min - Max                                   5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  
  Total dose administered                                                                        
    n                                               75                67                75       
    Mean (SD)                                 6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)
    Median                                        6720.0            6480.0            7200.0     
    Min - Max                                 4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0
  Total number of doses administered                                                             
    n                                               75                67                75       
    Mean (SD)                                    7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                          7.0               7.0               7.0      
    Min - Max                                    7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
  Total number of missed doses during study                                                      
    n                                               75                67                75       
    Mean (SD)                                   10.5 (5.9)        10.0 (6.1)         9.5 (5.5)   
    Median                                         10.0              11.0               9.0      
    Min - Max                                   0.0 - 20.0        0.0 - 19.0        0.0 - 20.0   
Drug B                                                                                           
  Overall duration (days)                                                                        
    n                                               59                67                57       
    Mean (SD)                                   77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  
    Median                                         79.0              76.0              65.0      
    Min - Max                                   2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  
  Total dose administered                                                                        
    n                                               59                67                57       
    Mean (SD)                                 6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)
    Median                                        6720.0            6240.0            6480.0     
    Min - Max                                 4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0
  Total number of doses administered                                                             
    n                                               59                67                57       
    Mean (SD)                                    7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                          7.0               7.0               7.0      
    Min - Max                                    7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
  Total number of missed doses during study                                                      
    n                                               59                67                57       
    Mean (SD)                                    9.1 (6.5)        10.0 (6.3)        10.7 (5.8)   
    Median                                          9.0              10.0              11.0      
    Min - Max                                   0.0 - 20.0        0.0 - 20.0        1.0 - 20.0

Experimental use!

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Code

# When summary table contains both categorical and numeric parameters,
# developer needs to do pre-processing to transform dataset to wide format.
adex_avalc_wide <- adex %>%
  filter(PARAMCD == "TDURD") %>%
  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, PARCAT2),
    names_from = PARAMCD,
    values_from = AVALC
  ) %>%
  mutate(
    TDURDC = factor(TDURD, levels = c("0 - 30", "31 - 60", "61 - 90", ">= 91"))
  ) %>%
  select(-TDURD)

anl <- adex %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),
    names_from = PARAMCD,
    values_from = AVAL
  ) %>%
  left_join(adex_avalc_wide, by = c("STUDYID", "USUBJID", "PARCAT2")) %>%
  var_relabel(
    TDOSE = "Total dose administered",
    TNDOSE = "Total number of doses administered",
    TDURD = "Overall duration (days)",
    TNDOSMIS = "Total number of missed doses during study",
    TDURDC = "Overall duration (days)"
  )

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  analyze_vars(
    vars = c("TDURD", "TDURDC", "TDOSE", "TNDOSE"),
    var_labels = var_labels(anl)[c("TDURD", "TDURDC", "TDOSE", "TNDOSE")]
  )

result <- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

result

                                          A: Drug X        B: Placebo      C: Combination 
Parameter Category (Drug A/Drug B)         (N=134)           (N=134)           (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————
Drug A                                                                                    
  Overall duration (days)                                                                 
    n                                        75                67                75       
    Mean (SD)                            74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  
    Median                                  77.0              80.0              78.0      
    Min - Max                            5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  
  Overall duration (days)                                                                 
    n                                        75                67                75       
    0 - 30                                12 (16%)         12 (17.9%)         15 (20%)    
    31 - 60                               18 (24%)         12 (17.9%)        14 (18.7%)   
    61 - 90                              19 (25.3%)        15 (22.4%)         18 (24%)    
    >= 91                                26 (34.7%)        28 (41.8%)        28 (37.3%)   
  Total dose administered                                                                 
    n                                        75                67                75       
    Mean (SD)                          6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)
    Median                                 6720.0            6480.0            7200.0     
    Min - Max                          4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0
  Total number of doses administered                                                      
    n                                        75                67                75       
    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                   7.0               7.0               7.0      
    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
Drug B                                                                                    
  Overall duration (days)                                                                 
    n                                        59                67                57       
    Mean (SD)                            77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  
    Median                                  79.0              76.0              65.0      
    Min - Max                            2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  
  Overall duration (days)                                                                 
    n                                        59                67                57       
    0 - 30                                8 (13.6%)        10 (14.9%)        16 (28.1%)   
    31 - 60                              14 (23.7%)        16 (23.9%)        12 (21.1%)   
    61 - 90                              15 (25.4%)        17 (25.4%)        11 (19.3%)   
    >= 91                                22 (37.3%)        24 (35.8%)        18 (31.6%)   
  Total dose administered                                                                 
    n                                        59                67                57       
    Mean (SD)                          6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)
    Median                                 6720.0            6240.0            6480.0     
    Min - Max                          4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0
  Total number of doses administered                                                      
    n                                        59                67                57       
    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                   7.0               7.0               7.0      
    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0

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.

Preview
Try this using WebR

Code

# When summary table contains both categorical and numeric parameters,
# developer needs to do pre-processing to transform dataset to wide format.
adex_avalc_wide <- adex %>%
  filter(PARAMCD == "TDURD") %>%
  select(STUDYID, USUBJID, PARAMCD, AVALC, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, PARCAT2),
    names_from = PARAMCD,
    values_from = AVALC
  ) %>%
  mutate(
    TDURDC = factor(TDURD, levels = c("0 - 30", "31 - 60", "61 - 90", ">= 91"))
  ) %>%
  select(-TDURD)

anl <- adex %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, AVAL, PARCAT2) %>%
  tidyr::pivot_wider(
    id_cols = c(STUDYID, USUBJID, ACTARM, PARCAT2),
    names_from = PARAMCD,
    values_from = AVAL
  ) %>%
  left_join(adex_avalc_wide, by = c("STUDYID", "USUBJID", "PARCAT2")) %>%
  var_relabel(
    TDOSE = "Total dose administered",
    TNDOSE = "Total number of doses administered",
    TDURD = "Overall duration (days)",
    TNDOSMIS = "Total number of missed doses during study",
    TDURDC = "Overall duration (days)"
  )

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARCAT2", split_label = "\nParameter Category (Drug A/Drug B)", label_pos = "topleft") %>%
  analyze_vars(
    vars = c("TDURD", "TDURDC", "TDOSE", "TNDOSE"),
    var_labels = var_labels(anl)[c("TDURD", "TDURDC", "TDOSE", "TNDOSE")]
  ) %>%
  count_missed_doses(
    "TNDOSMIS",
    thresholds = c(1, 5, 10, 15),
    var_labels = "Missed Doses"
  )

result <- build_table(lyt = lyt, df = anl, alt_counts_df = adsl)

result

                                          A: Drug X        B: Placebo      C: Combination 
Parameter Category (Drug A/Drug B)         (N=134)           (N=134)           (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————
Drug A                                                                                    
  Overall duration (days)                                                                 
    n                                        75                67                75       
    Mean (SD)                            74.3 (41.6)       79.0 (43.1)       74.2 (39.5)  
    Median                                  77.0              80.0              78.0      
    Min - Max                            5.0 - 149.0       2.0 - 150.0       1.0 - 147.0  
  Overall duration (days)                                                                 
    n                                        75                67                75       
    0 - 30                                12 (16%)         12 (17.9%)         15 (20%)    
    31 - 60                               18 (24%)         12 (17.9%)        14 (18.7%)   
    61 - 90                              19 (25.3%)        15 (22.4%)         18 (24%)    
    >= 91                                26 (34.7%)        28 (41.8%)        28 (37.3%)   
  Total dose administered                                                                 
    n                                        75                67                75       
    Mean (SD)                          6675.2 (1110.9)   6505.1 (1249.3)   6982.4 (1272.5)
    Median                                 6720.0            6480.0            7200.0     
    Min - Max                          4800.0 - 9360.0   4080.0 - 9360.0   4320.0 - 9360.0
  Total number of doses administered                                                      
    n                                        75                67                75       
    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                   7.0               7.0               7.0      
    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
  Missed Doses                                                                            
    n                                        75                67                75       
    At least 1 missed dose               74 (55.2%)         63 (47%)         73 (55.3%)   
    At least 5 missed doses               59 (44%)         49 (36.6%)        59 (44.7%)   
    At least 10 missed doses             41 (30.6%)        38 (28.4%)         37 (28%)    
    At least 15 missed doses             26 (19.4%)        21 (15.7%)        16 (12.1%)   
Drug B                                                                                    
  Overall duration (days)                                                                 
    n                                        59                67                57       
    Mean (SD)                            77.5 (40.5)       76.3 (41.0)       69.5 (46.2)  
    Median                                  79.0              76.0              65.0      
    Min - Max                            2.0 - 149.0       5.0 - 148.0       1.0 - 149.0  
  Overall duration (days)                                                                 
    n                                        59                67                57       
    0 - 30                                8 (13.6%)        10 (14.9%)        16 (28.1%)   
    31 - 60                              14 (23.7%)        16 (23.9%)        12 (21.1%)   
    61 - 90                              15 (25.4%)        17 (25.4%)        11 (19.3%)   
    >= 91                                22 (37.3%)        24 (35.8%)        18 (31.6%)   
  Total dose administered                                                                 
    n                                        59                67                57       
    Mean (SD)                          6630.5 (1334.0)   6297.3 (1291.1)   6505.3 (1080.7)
    Median                                 6720.0            6240.0            6480.0     
    Min - Max                          4320.0 - 8880.0   4080.0 - 9120.0   4320.0 - 8640.0
  Total number of doses administered                                                      
    n                                        59                67                57       
    Mean (SD)                             7.0 (0.0)         7.0 (0.0)         7.0 (0.0)   
    Median                                   7.0               7.0               7.0      
    Min - Max                             7.0 - 7.0         7.0 - 7.0         7.0 - 7.0   
  Missed Doses                                                                            
    n                                        59                67                57       
    At least 1 missed dose               56 (41.8%)         63 (47%)         57 (43.2%)   
    At least 5 missed doses              41 (30.6%)        50 (37.3%)        47 (35.6%)   
    At least 10 missed doses             27 (20.1%)        36 (26.9%)        31 (23.5%)   
    At least 15 missed doses             17 (12.7%)        18 (13.4%)        20 (15.2%)

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)
library(tidyr)

adsl <- random.cdisc.data::cadsl
adex <- random.cdisc.data::cadex

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adex <- df_explicit_na(adex)

adex <- adex %>%
  filter(PARCAT1 == "OVERALL") %>%
  select(STUDYID, USUBJID, ACTARM, PARAMCD, PARAM, AVAL, PARCAT2) %>%
  mutate(
    PARAMCD = as.character(PARAMCD),
    AVALC = ""
  ) %>%
  droplevels()

# Add new param tdurd for treatment duration.
set.seed(99)
tdurd_adsl <- adsl %>%
  select(STUDYID, USUBJID, ACTARM) %>%
  mutate(
    PARAMCD = "TDURD",
    PARAM = "Overall duration (days)",
    AVAL = sample(1:150, size = nrow(adsl), replace = TRUE),
    AVALC = case_when(
      0 <= AVAL & AVAL <= 30 ~ "0 - 30",
      31 <= AVAL & AVAL <= 60 ~ "31 - 60",
      61 <= AVAL & AVAL <= 90 ~ "61 - 90",
      TRUE ~ ">= 91"
    )
  )
tdurd <- adex %>%
  filter(PARAMCD == "TNDOSE") %>%
  select(STUDYID, USUBJID, PARCAT2) %>%
  left_join(tdurd_adsl, by = c("STUDYID", "USUBJID"))

# Add new param tndosmis for missed doses.
tndosmis_adsl <- adsl %>%
  select(STUDYID, USUBJID, ACTARM) %>%
  mutate(
    PARAMCD = "TNDOSMIS",
    PARAM = "Total number of missed doses during study",
    AVAL = sample(0:20, size = nrow(adsl), replace = TRUE),
    AVALC = ""
  )
tndosmis <- adex %>%
  filter(PARAMCD == "TNDOSE") %>%
  select(STUDYID, USUBJID, PARCAT2) %>%
  left_join(tndosmis_adsl, by = c("STUDYID", "USUBJID"))

adex <- dplyr::bind_rows(adex, tdurd, tndosmis) %>%
  mutate(PARAM = factor(
    PARAM,
    levels = c(
      "Overall duration (days)", "Total dose administered", "Total number of doses administered",
      "Total number of missed doses during study"
    )
  ))

Code

library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl
  ADEX <- random.cdisc.data::cadex
  adex_labels <- unname(col_labels(ADEX))
  ADEX <- ADEX %>%
    filter(PARCAT1 == "OVERALL") %>%
    mutate(
      AVALCAT1 = case_when(
        PARAMCD == "TDOSE" & AVAL < 5000 ~ "LOW",
        PARAMCD == "TDOSE" & AVAL >= 5000 ~ "HIGH",
        PARAMCD == "TNDOSE" & AVAL < 10 ~ "< 10",
        PARAMCD == "TNDOSE" & AVAL >= 10 ~ ">= 10"
      )
    )
  col_labels(ADEX) <- c(adex_labels, "")
})
datanames <- c("ADSL", "ADEX")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADEX <- data[["ADEX"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Exposure Table",
      dataname = "ADEX",
      arm_var = choices_selected(
        choices = variable_choices(ADEX, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEX, c("PARCAT2", "PARAM")),
        selected = c("PARCAT2", "PARAM")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADEX, c("AVAL", "AVALCAT1")),
        selected = c("AVAL", "AVALCAT1")
      ),
      paramcd = choices_selected(
        choices = value_choices(ADEX, "PARAMCD", "PARAM"),
        selected = c("TDOSE", "TNDOSE")
      ),
      denominator = choices_selected(
        choices = c("n", "N", "omit"),
        selected = "N"
      )
    )
  ), # Set initial filter state as single study drug to produce smaller table
  filter = teal_slices(
    teal_slice("ADEX", "PARCAT2", selected = "Drug A"),
    teal_slice("ADEX", "AVAL", selected = NULL)
  )
)

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://pharmaverse.r-universe.dev", getOption("webr_pkg_repos")))

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

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

  ADSL <- random.cdisc.data::cadsl
  ADEX <- random.cdisc.data::cadex
  adex_labels <- unname(col_labels(ADEX))
  ADEX <- ADEX %>%
    filter(PARCAT1 == "OVERALL") %>%
    mutate(
      AVALCAT1 = case_when(
        PARAMCD == "TDOSE" & AVAL < 5000 ~ "LOW",
        PARAMCD == "TDOSE" & AVAL >= 5000 ~ "HIGH",
        PARAMCD == "TNDOSE" & AVAL < 10 ~ "< 10",
        PARAMCD == "TNDOSE" & AVAL >= 10 ~ ">= 10"
      )
    )
  col_labels(ADEX) <- c(adex_labels, "")
})
datanames <- c("ADSL", "ADEX")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADEX <- data[["ADEX"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Exposure Table",
      dataname = "ADEX",
      arm_var = choices_selected(
        choices = variable_choices(ADEX, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEX, c("PARCAT2", "PARAM")),
        selected = c("PARCAT2", "PARAM")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADEX, c("AVAL", "AVALCAT1")),
        selected = c("AVAL", "AVALCAT1")
      ),
      paramcd = choices_selected(
        choices = value_choices(ADEX, "PARAMCD", "PARAM"),
        selected = c("TDOSE", "TNDOSE")
      ),
      denominator = choices_selected(
        choices = c("n", "N", "omit"),
        selected = "N"
      )
    )
  ), # Set initial filter state as single study drug to produce smaller table
  filter = teal_slices(
    teal_slice("ADEX", "PARCAT2", selected = "Drug A"),
    teal_slice("ADEX", "AVAL", selected = NULL)
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:15:19 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
 backports               1.5.0       2024-05-23 [1] CRAN (R 4.4.1)
 brio                    1.1.5       2024-04-24 [1] CRAN (R 4.4.1)
 broom                   1.0.7       2024-09-26 [1] CRAN (R 4.4.1)
 bslib                   0.8.0       2024-07-29 [1] CRAN (R 4.4.1)
 cachem                  1.1.0       2024-05-16 [1] CRAN (R 4.4.1)
 callr                   3.7.6       2024-03-25 [1] CRAN (R 4.4.1)
 checkmate               2.3.2       2024-07-29 [1] CRAN (R 4.4.1)
 chromote                0.3.1       2024-08-30 [1] CRAN (R 4.4.1)
 cli                     3.6.3       2024-06-21 [1] CRAN (R 4.4.1)
 coda                    0.19-4.1    2024-01-31 [1] CRAN (R 4.4.1)
 codetools               0.2-20      2024-03-31 [2] CRAN (R 4.4.1)
 colorspace              2.1-1       2024-07-26 [1] CRAN (R 4.4.1)
 data.table              1.16.2      2024-10-10 [1] CRAN (R 4.4.1)
 digest                  0.6.37      2024-08-19 [1] CRAN (R 4.4.1)
 dplyr                 * 1.1.4       2023-11-17 [1] CRAN (R 4.4.1)
 emmeans                 1.10.5      2024-10-14 [1] CRAN (R 4.4.1)
 estimability            1.5.1       2024-05-12 [1] CRAN (R 4.4.1)
 evaluate                1.0.1       2024-10-10 [1] CRAN (R 4.4.1)
 fansi                   1.0.6       2023-12-08 [1] CRAN (R 4.4.1)
 fastmap                 1.2.0       2024-05-15 [1] CRAN (R 4.4.1)
 fontawesome             0.5.2       2023-08-19 [1] CRAN (R 4.4.1)
 forcats                 1.0.0       2023-01-29 [1] CRAN (R 4.4.1)
 formatR                 1.14        2023-01-17 [1] CRAN (R 4.4.1)
 formatters            * 0.5.9.9003  2024-11-02 [1] git2r (https://github.com/insightsengineering/formatters.git@84e8c3e)
 geepack                 1.3.12      2024-09-23 [1] CRAN (R 4.4.1)
 generics                0.1.3       2022-07-05 [1] CRAN (R 4.4.1)
 ggplot2                 3.5.1       2024-04-23 [1] CRAN (R 4.4.1)
 glue                    1.8.0       2024-09-30 [1] CRAN (R 4.4.1)
 gtable                  0.3.5       2024-04-22 [1] CRAN (R 4.4.1)
 htmltools               0.5.8.1     2024-04-04 [1] CRAN (R 4.4.1)
 htmlwidgets             1.6.4       2023-12-06 [1] CRAN (R 4.4.1)
 httpuv                  1.6.15      2024-03-26 [1] CRAN (R 4.4.1)
 httr                    1.4.7       2023-08-15 [1] CRAN (R 4.4.1)
 jquerylib               0.1.4       2021-04-26 [1] CRAN (R 4.4.1)
 jsonlite                1.8.9       2024-09-20 [1] CRAN (R 4.4.1)
 knitr                   1.48        2024-07-07 [1] CRAN (R 4.4.1)
 later                   1.3.2       2023-12-06 [1] CRAN (R 4.4.1)
 lattice                 0.22-6      2024-03-20 [2] CRAN (R 4.4.1)
 lazyeval                0.2.2       2019-03-15 [1] CRAN (R 4.4.1)
 lifecycle               1.0.4       2023-11-07 [1] CRAN (R 4.4.1)
 logger                  0.3.0       2024-03-05 [1] CRAN (R 4.4.1)
 magrittr              * 2.0.3       2022-03-30 [1] CRAN (R 4.4.1)
 MASS                    7.3-61      2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-0       2024-04-26 [1] CRAN (R 4.4.1)
 memoise                 2.0.1       2021-11-26 [1] CRAN (R 4.4.1)
 mime                    0.12        2021-09-28 [1] CRAN (R 4.4.1)
 multcomp                1.4-26      2024-07-18 [1] CRAN (R 4.4.1)
 munsell                 0.5.1       2024-04-01 [1] CRAN (R 4.4.1)
 mvtnorm                 1.3-1       2024-09-03 [1] CRAN (R 4.4.1)
 nestcolor               0.1.2.9017  2024-11-02 [1] git2r (https://github.com/insightsengineering/nestcolor.git@3d2ce62)
 nlme                    3.1-166     2024-08-14 [2] CRAN (R 4.4.1)
 pillar                  1.9.0       2023-03-22 [1] CRAN (R 4.4.1)
 pkgconfig               2.0.3       2019-09-22 [1] CRAN (R 4.4.1)
 plotly                  4.10.4      2024-01-13 [1] CRAN (R 4.4.1)
 processx                3.8.4       2024-03-16 [1] CRAN (R 4.4.1)
 promises                1.3.0       2024-04-05 [1] CRAN (R 4.4.1)
 ps                      1.8.0       2024-09-12 [1] CRAN (R 4.4.1)
 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       0.3.16.9001 2024-11-02 [1] git2r (https://github.com/insightsengineering/random.cdisc.data.git@a65bfbe)
 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)
 Rdpack                  2.6.1       2024-08-06 [1] CRAN (R 4.4.1)
 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)
 rtables               * 0.6.10.9002 2024-11-02 [1] git2r (https://github.com/insightsengineering/rtables.git@d2cc7a0)
 sandwich                3.1-1       2024-09-15 [1] CRAN (R 4.4.1)
 sass                    0.4.9       2024-03-15 [1] CRAN (R 4.4.1)
 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                  * 0.15.2.9079 2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.git@53c423d)
 teal.code             * 0.5.0.9012  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.code.git@86aa265)
 teal.data             * 0.6.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.data.git@eca9695)
 teal.logger             0.3.0.9000  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.logger.git@cd9cc7e)
 teal.modules.clinical * 0.9.1.9030  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.modules.clinical.git@5cf76ac)
 teal.reporter           0.3.1.9016  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.reporter.git@4fefa7e)
 teal.slice            * 0.5.1.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.slice.git@25f4101)
 teal.transform        * 0.5.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.transform.git@0b855e3)
 teal.widgets            0.4.2.9022  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.widgets.git@8a39dd4)
 tern                  * 0.9.6.9013  2024-11-02 [1] git2r (https://github.com/insightsengineering/tern.git@435fd51)
 tern.gee                0.1.5.9004  2024-11-02 [1] git2r (https://github.com/insightsengineering/tern.gee.git@458172d)
 testthat                3.2.1.1     2024-04-14 [1] CRAN (R 4.4.1)
 TH.data                 1.1-2       2023-04-17 [1] CRAN (R 4.4.1)
 tibble                  3.2.1       2023-03-20 [1] CRAN (R 4.4.1)
 tidyr                 * 1.3.1       2024-01-24 [1] CRAN (R 4.4.1)
 tidyselect              1.2.1       2024-03-11 [1] CRAN (R 4.4.1)
 utf8                    1.2.4       2023-10-22 [1] CRAN (R 4.4.1)
 vctrs                   0.6.5       2023-12-01 [1] CRAN (R 4.4.1)
 viridisLite             0.4.2       2023-05-02 [1] CRAN (R 4.4.1)
 webshot                 0.5.5       2023-06-26 [1] CRAN (R 4.4.1)
 webshot2                0.1.1       2023-08-11 [1] CRAN (R 4.4.1)
 websocket               1.4.2       2024-07-22 [1] CRAN (R 4.4.1)
 withr                   3.0.1       2024-07-31 [1] CRAN (R 4.4.1)
 xfun                    0.48        2024-10-03 [1] CRAN (R 4.4.1)
 xtable                  1.8-4       2019-04-21 [1] CRAN (R 4.4.1)
 yaml                    2.3.10      2024-07-26 [1] CRAN (R 4.4.1)
 zoo                     1.8-12      2023-04-13 [1] CRAN (R 4.4.1)

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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