DMT01

Demographics and Baseline Characteristics

---

Table with an Additional Study-Specific Continuous Variable

Preview

vars <- c("AGE", "AGEGR1", "SEX", "ETHNIC", "RACE", "BMRKR1")
var_labels <- c(
  "Age (yr)",
  "Age Group",
  "Sex",
  "Ethnicity",
  "Race",
  "Continous Level Biomarker 1"
)

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  add_overall_col("All Patients") %>%
  analyze_vars(
    vars = vars,
    var_labels = var_labels
  ) %>%
  build_table(adsl)

result

                                               A: Drug X    B: Placebo    C: Combination   All Patients
                                                (N=134)       (N=134)        (N=132)         (N=400)   
———————————————————————————————————————————————————————————————————————————————————————————————————————
Age (yr)                                                                                               
  n                                               134           134            132             400     
  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4) 
  Median                                         33.0          35.0            35.0            34.0    
  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0     20.0 - 69.0 
Age Group                                                                                              
  n                                               134           134            132             400     
  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)     322 (80.5%) 
  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)      77 (19.2%) 
  >=65                                             0             0           1 (0.8%)        1 (0.2%)  
Sex                                                                                                    
  n                                               134           134            132             400     
  Female                                       79 (59%)     82 (61.2%)       70 (53%)      231 (57.8%) 
  Male                                         55 (41%)     52 (38.8%)       62 (47%)      169 (42.2%) 
Ethnicity                                                                                              
  n                                               134           134            132             400     
  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)       48 (12%)  
  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)      308 (77%)  
  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)       27 (6.8%)  
  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)       17 (4.2%)  
Race                                                                                                   
  n                                               134           134            132             400     
  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)      208 (52%)  
  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)      91 (22.8%) 
  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)      74 (18.5%) 
  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)       25 (6.2%)  
  MULTIPLE                                         0         1 (0.7%)           0            1 (0.2%)  
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0            1 (0.2%)  
  OTHER                                            0             0              0               0      
  UNKNOWN                                          0             0              0               0      
Continous Level Biomarker 1                                                                            
  n                                               134           134            132             400     
  Mean (SD)                                    6.0 (3.6)     5.7 (3.3)      5.6 (3.5)       5.8 (3.4)  
  Median                                          5.4           4.8            4.6             4.8     
  Min - Max                                   0.4 - 17.7    0.6 - 14.2      0.2 - 21.4      0.2 - 21.4 

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Table with an Additional Study-Specific Categorical Variable

Preview

vars <- c("AGE", "AGEGR1", "SEX", "ETHNIC", "RACE", "BMRKR1_CAT")
var_labels <- c(
  "Age (yr)",
  "Age Group",
  "Sex",
  "Ethnicity",
  "Race",
  "Biomarker 1 Categories"
)

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  analyze_vars(
    vars = vars,
    var_labels = var_labels
  ) %>%
  build_table(adsl)

result

                                               A: Drug X    B: Placebo    C: Combination
                                                (N=134)       (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————————————————————————
Age (yr)                                                                                
  n                                               134           134            132      
  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)  
  Median                                         33.0          35.0            35.0     
  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0  
Age Group                                                                               
  n                                               134           134            132      
  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)  
  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)  
  >=65                                             0             0           1 (0.8%)   
Sex                                                                                     
  n                                               134           134            132      
  Female                                       79 (59%)     82 (61.2%)       70 (53%)   
  Male                                         55 (41%)     52 (38.8%)       62 (47%)   
Ethnicity                                                                               
  n                                               134           134            132      
  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)  
  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)  
  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)   
  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)   
Race                                                                                    
  n                                               134           134            132      
  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)  
  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)  
  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)  
  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)   
  MULTIPLE                                         0         1 (0.7%)           0       
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0       
  OTHER                                            0             0              0       
  UNKNOWN                                          0             0              0       
Biomarker 1 Categories                                                                  
  n                                               134           134            132      
  LOW                                         33 (24.6%)    41 (30.6%)      38 (28.8%)  
  MEDIUM                                      84 (62.7%)    76 (56.7%)      80 (60.6%)  
  HIGH                                        17 (12.7%)    17 (12.7%)      14 (10.6%)  

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Table with Subgrouping for Some Analyses

Preview

split_fun <- drop_split_levels

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  analyze_vars(
    vars = c("AGE", "SEX", "RACE"),
    var_labels = c("Age", "Sex", "Race")
  ) %>%
  split_rows_by("STRATA1",
    split_fun = split_fun
  ) %>%
  analyze_vars("BMRKR1") %>%
  build_table(adsl)

result

                                               A: Drug X    B: Placebo    C: Combination
                                                (N=134)       (N=134)        (N=132)    
————————————————————————————————————————————————————————————————————————————————————————
Age                                                                                     
  n                                               134           134            132      
  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)  
  Median                                         33.0          35.0            35.0     
  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0  
Sex                                                                                     
  n                                               134           134            132      
  Female                                       79 (59%)     82 (61.2%)       70 (53%)   
  Male                                         55 (41%)     52 (38.8%)       62 (47%)   
Race                                                                                    
  n                                               134           134            132      
  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)  
  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)  
  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)  
  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)   
  MULTIPLE                                         0         1 (0.7%)           0       
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0       
  OTHER                                            0             0              0       
  UNKNOWN                                          0             0              0       
A                                                                                       
  n                                               38            44              40      
  Mean (SD)                                    5.8 (3.8)     5.4 (3.2)      5.1 (3.2)   
  Median                                          5.1           4.5            3.8      
  Min - Max                                   0.4 - 17.7    1.4 - 14.2      1.5 - 14.0  
B                                                                                       
  n                                               47            45              43      
  Mean (SD)                                    6.1 (3.6)     5.8 (3.6)      5.7 (3.4)   
  Median                                          5.2           4.8            5.1      
  Min - Max                                   1.6 - 17.2    0.6 - 13.3      0.2 - 16.5  
C                                                                                       
  n                                               49            45              49      
  Mean (SD)                                    6.0 (3.4)     5.9 (3.2)      6.0 (3.8)   
  Median                                          5.8           5.6            4.5      
  Min - Max                                   0.5 - 15.1    1.5 - 13.9      1.2 - 21.4  

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Table with Additional Vital Signs Baseline Values

Preview

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  analyze_vars(
    vars = c("AGE", "SEX", "RACE", "DBP", "SBP"),
    var_labels = c(
      "Age (yr)",
      "Sex",
      "Race",
      "Diastolic Blood Pressure",
      "Systolic Blood Pressure"
    )
  ) %>%
  build_table(adsl)

result

                                               A: Drug X      B: Placebo    C: Combination
                                                (N=134)        (N=134)         (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————
Age (yr)                                                                                  
  n                                               134            134             132      
  Mean (SD)                                    33.8 (6.6)     35.4 (7.9)      35.4 (7.7)  
  Median                                          33.0           35.0            35.0     
  Min - Max                                   21.0 - 50.0    21.0 - 62.0     20.0 - 69.0  
Sex                                                                                       
  n                                               134            134             132      
  Female                                        79 (59%)      82 (61.2%)       70 (53%)   
  Male                                          55 (41%)      52 (38.8%)       62 (47%)   
Race                                                                                      
  n                                               134            134             132      
  ASIAN                                        68 (50.7%)      67 (50%)       73 (55.3%)  
  BLACK OR AFRICAN AMERICAN                    31 (23.1%)     28 (20.9%)      32 (24.2%)  
  WHITE                                        27 (20.1%)     26 (19.4%)      21 (15.9%)  
  AMERICAN INDIAN OR ALASKA NATIVE               8 (6%)       11 (8.2%)        6 (4.5%)   
  MULTIPLE                                         0           1 (0.7%)           0       
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0           1 (0.7%)           0       
  OTHER                                            0              0               0       
  UNKNOWN                                          0              0               0       
Diastolic Blood Pressure                                                                  
  n                                               134            134             132      
  Mean (SD)                                   96.5 (19.9)    101.1 (19.9)    102.8 (19.5) 
  Median                                          96.0          100.4           102.0     
  Min - Max                                   44.3 - 136.6   29.2 - 143.8    49.4 - 153.5 
Systolic Blood Pressure                                                                   
  n                                               134            134             132      
  Mean (SD)                                   151.7 (31.5)   149.5 (26.5)    144.7 (30.1) 
  Median                                         150.1          153.0           146.5     
  Min - Max                                   69.1 - 231.2   87.2 - 220.9    71.8 - 220.2 

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Table with Additional Values from ADSUB

Preview

result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  analyze_vars(
    vars = c("AGE", "SEX", "RACE", "BBMISI"),
    var_labels = c(
      "Age (yr)",
      "Sex",
      "Race",
      "Baseline BMI"
    )
  ) %>%
  build_table(adsl)

result

                                               A: Drug X     B: Placebo     C: Combination
                                                (N=134)        (N=134)         (N=132)    
——————————————————————————————————————————————————————————————————————————————————————————
Age (yr)                                                                                  
  n                                               134            134             132      
  Mean (SD)                                   33.8 (6.6)     35.4 (7.9)       35.4 (7.7)  
  Median                                         33.0           35.0             35.0     
  Min - Max                                   21.0 - 50.0    21.0 - 62.0     20.0 - 69.0  
Sex                                                                                       
  n                                               134            134             132      
  Female                                       79 (59%)      82 (61.2%)        70 (53%)   
  Male                                         55 (41%)      52 (38.8%)        62 (47%)   
Race                                                                                      
  n                                               134            134             132      
  ASIAN                                       68 (50.7%)      67 (50%)        73 (55.3%)  
  BLACK OR AFRICAN AMERICAN                   31 (23.1%)     28 (20.9%)       32 (24.2%)  
  WHITE                                       27 (20.1%)     26 (19.4%)       21 (15.9%)  
  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)        11 (8.2%)        6 (4.5%)   
  MULTIPLE                                         0          1 (0.7%)            0       
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0          1 (0.7%)            0       
  OTHER                                            0              0               0       
  UNKNOWN                                          0              0               0       
Baseline BMI                                                                              
  n                                               134            134             132      
  Mean (SD)                                   30.0 (18.3)    32.4 (23.2)     30.1 (18.4)  
  Median                                         27.1           31.1             30.0     
  Min - Max                                   -6.9 - 75.9   -26.6 - 117.9    -44.2 - 87.5 

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Data Setup

library(tern)
library(dplyr)
library(tidyr)

adsl <- random.cdisc.data::cadsl
advs <- random.cdisc.data::cadvs
adsub <- random.cdisc.data::cadsub

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

# Change description in variable SEX.
adsl <- adsl %>%
  mutate(
    SEX = factor(case_when(
      SEX == "M" ~ "Male",
      SEX == "F" ~ "Female",
      SEX == "U" ~ "Unknown",
      SEX == "UNDIFFERENTIATED" ~ "Undifferentiated"
    )),
    AGEGR1 = factor(
      case_when(
        between(AGE, 18, 40) ~ "18-40",
        between(AGE, 41, 64) ~ "41-64",
        AGE > 64 ~ ">=65"
      ),
      levels = c("18-40", "41-64", ">=65")
    ),
    BMRKR1_CAT = factor(
      case_when(
        BMRKR1 < 3.5 ~ "LOW",
        BMRKR1 >= 3.5 & BMRKR1 < 10 ~ "MEDIUM",
        BMRKR1 >= 10 ~ "HIGH"
      ),
      levels = c("LOW", "MEDIUM", "HIGH")
    )
  ) %>%
  var_relabel(
    BMRKR1_CAT = "Biomarker 1 Categories"
  )
# The developer needs to do pre-processing to add necessary variables based on ADVS to analysis dataset.
# Obtain SBP, DBP and weight.
get_param_advs <- function(pname, plabel) {
  ds <- advs %>%
    filter(PARAM == plabel & AVISIT == "BASELINE") %>%
    select(USUBJID, AVAL)

  colnames(ds) <- c("USUBJID", pname)

  ds
}
# The developer needs to do pre-processing to add necessary variables based on ADSUB to analysis dataset.
# Obtain baseline BMI (BBMISI).
get_param_adsub <- function(pname, plabel) {
  ds <- adsub %>%
    filter(PARAM == plabel) %>%
    select(USUBJID, AVAL)

  colnames(ds) <- c("USUBJID", pname)

  ds
}
adsl <- adsl %>%
  left_join(get_param_advs("SBP", "Systolic Blood Pressure"), by = "USUBJID") %>%
  left_join(get_param_advs("DBP", "Diastolic Blood Pressure"), by = "USUBJID") %>%
  left_join(get_param_advs("WGT", "Weight"), by = "USUBJID") %>%
  left_join(get_param_adsub("BBMISI", "Baseline BMI"), by = "USUBJID")

teal App

Preview

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl

  # Include `EOSDY` and `DCSREAS` variables below because they contain missing data.
  stopifnot(
    any(is.na(ADSL$EOSDY)),
    any(is.na(ADSL$DCSREAS))
  )
})
datanames <- "ADSL"
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary(
      label = "Demographic Table",
      dataname = "ADSL",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      summarize_vars = choices_selected(
        c("SEX", "RACE", "BMRKR2", "EOSDY", "DCSREAS"),
        c("SEX", "RACE")
      ),
      useNA = "ifany"
    )
  )
)

shinyApp(app$ui, app$server)

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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, {
  ADSL <- random.cdisc.data::cadsl

  # Include `EOSDY` and `DCSREAS` variables below because they contain missing data.
  stopifnot(
    any(is.na(ADSL$EOSDY)),
    any(is.na(ADSL$DCSREAS))
  )
})
datanames <- "ADSL"
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary(
      label = "Demographic Table",
      dataname = "ADSL",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      summarize_vars = choices_selected(
        c("SEX", "RACE", "BMRKR2", "EOSDY", "DCSREAS"),
        c("SEX", "RACE")
      ),
      useNA = "ifany"
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 17:50:47 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)
 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)
 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)
 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)
 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)
 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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