RSPT01

Best Overall Response

The tabulation layout is built in layers for the analysis of overall response and applied to the pre-processed dataset.

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Code

lyt_01 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM", ref_group = "A: Drug X") %>%
  estimate_proportion(
    vars = "is_rsp",
    table_names = "est_prop"
  ) %>%
  estimate_proportion_diff(
    vars = "is_rsp",
    show_labels = "visible",
    var_labels = "Unstratified Analysis",
    table_names = "est_prop_diff"
  ) %>%
  test_proportion_diff(
    vars = "is_rsp",
    table_names = "test_prop_diff"
  ) %>%
  estimate_odds_ratio(
    vars = "is_rsp",
    table_names = "est_or"
  ) %>%
  estimate_multinomial_response(var = "rsp_lab")

result <- build_table(lyt = lyt_01, df = anl)
result

                                       A: Drug X          B: Placebo         C: Combination  
                                        (N=134)            (N=134)              (N=132)      
—————————————————————————————————————————————————————————————————————————————————————————————
Responders                            100 (74.6%)         84 (62.7%)           81 (61.4%)    
95% CI (Wald, with correction)        (66.9, 82.4)       (54.1, 71.2)         (52.7, 70.0)   
Unstratified Analysis                                                                        
  Difference in Response rate (%)                           -11.9                -13.3       
    95% CI (Wald, with correction)                      (-23.7, -0.2)        (-25.1, -1.4)   
  p-value (Chi-Squared Test)                                0.0351               0.0204      
Odds Ratio (95% CI)                                   0.57 (0.34 - 0.96)   0.54 (0.32 - 0.91)
Complete Response (CR)                 60 (44.8%)         47 (35.1%)           57 (43.2%)    
  95% CI (Wald, with correction)     (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  
Partial Response (PR)                  40 (29.9%)         37 (27.6%)           24 (18.2%)    
  95% CI (Wald, with correction)     (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  
Stable Disease (SD)                     9 (6.7%)          22 (16.4%)           13 (9.8%)     
  95% CI (Wald, with correction)     (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   
Progressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    
  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  
Not Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     
  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)

Experimental use!

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Remove (or add) rows of results by removing/adding the corresponding layers from the layout. For instance, the odds-ratio row is removed by simply removing the estimate_odds_ratio call:

Code

lyt_02 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM", ref_group = "A: Drug X") %>%
  estimate_proportion(
    vars = "is_rsp",
    table_names = "est_prop"
  ) %>%
  estimate_proportion_diff(
    vars = "is_rsp",
    show_labels = "visible",
    var_labels = "Unstratified Analysis",
    table_names = "est_prop_diff"
  ) %>%
  test_proportion_diff(
    vars = "is_rsp",
    table_names = "test_prop"
  ) %>%
  estimate_multinomial_response(var = "rsp_lab")

result <- build_table(lyt = lyt_02, df = anl)
result

                                       A: Drug X        B: Placebo     C: Combination
                                        (N=134)          (N=134)          (N=132)    
—————————————————————————————————————————————————————————————————————————————————————
Responders                            100 (74.6%)       84 (62.7%)       81 (61.4%)  
95% CI (Wald, with correction)        (66.9, 82.4)     (54.1, 71.2)     (52.7, 70.0) 
Unstratified Analysis                                                                
  Difference in Response rate (%)                         -11.9            -13.3     
    95% CI (Wald, with correction)                    (-23.7, -0.2)    (-25.1, -1.4) 
  p-value (Chi-Squared Test)                              0.0351           0.0204    
Complete Response (CR)                 60 (44.8%)       47 (35.1%)       57 (43.2%)  
  95% CI (Wald, with correction)     (35.98, 53.57)   (26.62, 43.53)   (34.35, 52.01)
Partial Response (PR)                  40 (29.9%)       37 (27.6%)       24 (18.2%)  
  95% CI (Wald, with correction)     (21.73, 37.97)   (19.67, 35.55)   (11.22, 25.14)
Stable Disease (SD)                     9 (6.7%)        22 (16.4%)       13 (9.8%)   
  95% CI (Wald, with correction)     (2.11, 11.33)    (9.77, 23.06)    (4.39, 15.31) 
Progressive Disease (PD)               24 (17.9%)       16 (11.9%)       33 (25.0%)  
  95% CI (Wald, with correction)     (11.05, 24.78)   (6.08, 17.80)    (17.23, 32.77)
Not Evaluable (NE)                      1 (0.7%)        12 (9.0%)         5 (3.8%)   
  95% CI (Wald, with correction)      (0.00, 2.58)    (3.75, 14.16)     (0.15, 7.42)

Experimental use!

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The confidence level is controlled by the conf_level parameter to the estimation functions. Similarly, the methods for tests and confidence interval can be modified (see ?estimate_proportion_diff).

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Code

conf_level <- 0.90
lyt_03 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM", ref_group = "A: Drug X") %>%
  estimate_proportion(
    vars = "is_rsp",
    conf_level = conf_level,
    method = "clopper-pearson",
    table_names = "est_prop"
  ) %>%
  estimate_proportion_diff(
    vars = "is_rsp",
    show_labels = "visible",
    var_labels = "Unstratified Analysis",
    conf_level = conf_level,
    method = "ha",
    table_names = "est_prop_diff"
  ) %>%
  test_proportion_diff(
    vars = "is_rsp",
    method = "fisher",
    table_names = "test_prop"
  ) %>%
  estimate_odds_ratio(
    vars = "is_rsp",
    conf_level = conf_level,
    table_names = "est_or"
  ) %>%
  estimate_multinomial_response(
    var = "rsp_lab",
    conf_level = conf_level,
    method = "clopper-pearson"
  )

result <- build_table(lyt = lyt_03, df = anl)
result

                                      A: Drug X          B: Placebo         C: Combination  
                                       (N=134)            (N=134)              (N=132)      
————————————————————————————————————————————————————————————————————————————————————————————
Responders                           100 (74.6%)         84 (62.7%)           81 (61.4%)    
90% CI (Clopper-Pearson)             (67.7, 80.7)       (55.3, 69.7)         (53.9, 68.5)   
Unstratified Analysis                                                                       
  Difference in Response rate (%)                          -11.9                -13.3       
    90% CI (Anderson-Hauck)                            (-21.6, -2.3)        (-23.0, -3.5)   
  p-value (Fisher's Exact Test)                            0.0479               0.0253      
Odds Ratio (90% CI)                                  0.57 (0.37 - 0.89)   0.54 (0.35 - 0.84)
Complete Response (CR)                60 (44.8%)         47 (35.1%)           57 (43.2%)    
  90% CI (Clopper-Pearson)          (37.48, 52.25)     (28.22, 42.43)       (35.88, 50.71)  
Partial Response (PR)                 40 (29.9%)         37 (27.6%)           24 (18.2%)    
  90% CI (Clopper-Pearson)          (23.36, 37.02)     (21.31, 34.67)       (12.87, 24.61)  
Stable Disease (SD)                    9 (6.7%)          22 (16.4%)           13 (9.8%)     
  90% CI (Clopper-Pearson)          (3.55, 11.43)      (11.38, 22.61)       (5.92, 15.20)   
Progressive Disease (PD)              24 (17.9%)         16 (11.9%)           33 (25.0%)    
  90% CI (Clopper-Pearson)          (12.67, 24.25)     (7.63, 17.57)        (18.90, 31.97)  
Not Evaluable (NE)                     1 (0.7%)          12 (9.0%)             5 (3.8%)     
  90% CI (Clopper-Pearson)           (0.04, 3.49)      (5.25, 14.11)         (1.50, 7.80)

Experimental use!

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The stratified analysis section can be added by defining the analyses needed with control_binary_comparison for the argument strat_analysis and identifying the stratification variables to use.

Code

strata <- "STRATA1"
lyt_04 <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM", ref_group = "A: Drug X") %>%
  estimate_proportion(
    vars = "is_rsp",
    table_names = "est_prop"
  ) %>%
  estimate_proportion_diff(
    vars = "is_rsp",
    show_labels = "visible",
    var_labels = "Unstratified Analysis",
    table_names = "est_prop_diff"
  ) %>%
  test_proportion_diff(
    vars = "is_rsp",
    table_names = "test_prop"
  ) %>%
  estimate_odds_ratio(
    vars = "is_rsp",
    table_names = "est_or"
  ) %>%
  estimate_proportion_diff(
    vars = "is_rsp",
    show_labels = "visible",
    var_labels = "Stratified Analysis",
    method = "cmh",
    variables = list(strata = strata),
    table_names = "est_prop_diff_strat"
  ) %>%
  test_proportion_diff(
    vars = "is_rsp",
    method = "cmh",
    variables = list(strata = strata),
    table_names = "test_prop_strat"
  ) %>%
  estimate_odds_ratio(
    vars = "is_rsp",
    variables = list(strata = strata, arm = "ARM"),
    table_names = "est_or_strat"
  ) %>%
  estimate_multinomial_response(var = "rsp_lab")

result <- build_table(lyt = lyt_04, df = anl)
result

                                             A: Drug X          B: Placebo         C: Combination  
                                              (N=134)            (N=134)              (N=132)      
———————————————————————————————————————————————————————————————————————————————————————————————————
Responders                                  100 (74.6%)         84 (62.7%)           81 (61.4%)    
95% CI (Wald, with correction)              (66.9, 82.4)       (54.1, 71.2)         (52.7, 70.0)   
Unstratified Analysis                                                                              
  Difference in Response rate (%)                                 -11.9                -13.3       
    95% CI (Wald, with correction)                            (-23.7, -0.2)        (-25.1, -1.4)   
  p-value (Chi-Squared Test)                                      0.0351               0.0204      
Odds Ratio (95% CI)                                         0.57 (0.34 - 0.96)   0.54 (0.32 - 0.91)
Stratified Analysis                                                                                
  Difference in Response rate (%)                                 -11.9                -13.5       
    95% CI (CMH, without correction)                          (-22.7, -1.0)        (-24.5, -2.5)   
  p-value (Cochran-Mantel-Haenszel Test)                          0.0366               0.0180      
Odds Ratio (95% CI)                                         0.57 (0.34 - 0.96)   0.54 (0.32 - 0.90)
Complete Response (CR)                       60 (44.8%)         47 (35.1%)           57 (43.2%)    
  95% CI (Wald, with correction)           (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  
Partial Response (PR)                        40 (29.9%)         37 (27.6%)           24 (18.2%)    
  95% CI (Wald, with correction)           (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  
Stable Disease (SD)                           9 (6.7%)          22 (16.4%)           13 (9.8%)     
  95% CI (Wald, with correction)           (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   
Progressive Disease (PD)                     24 (17.9%)         16 (11.9%)           33 (25.0%)    
  95% CI (Wald, with correction)           (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  
Not Evaluable (NE)                            1 (0.7%)          12 (9.0%)             5 (3.8%)     
  95% CI (Wald, with correction)            (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)

Experimental use!

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The definition of responders is realized during the pre-processing step. The layout does not need to be modified and can be reused.

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Code

anl <- anl_adsl %>%
  left_join(anl_adrs, by = c("STUDYID", "USUBJID")) %>%
  mutate(rsp_lab = d_onco_rsp_label(AVALC)) %>%
  mutate(is_rsp = AVALC == "CR") %>%
  mutate(ARM = relevel(ARM, ref = "A: Drug X")) %>%
  var_relabel(ARM = "Description of Planned Arm")

result <- build_table(lyt = lyt_01, df = anl)
result

                                       A: Drug X          B: Placebo         C: Combination  
                                        (N=134)            (N=134)              (N=132)      
—————————————————————————————————————————————————————————————————————————————————————————————
Responders                             60 (44.8%)         47 (35.1%)           57 (43.2%)    
95% CI (Wald, with correction)        (36.0, 53.6)       (26.6, 43.5)         (34.4, 52.0)   
Unstratified Analysis                                                                        
  Difference in Response rate (%)                            -9.7                 -1.6       
    95% CI (Wald, with correction)                       (-22.1, 2.7)        (-14.3, 11.1)   
  p-value (Chi-Squared Test)                                0.1049               0.7934      
Odds Ratio (95% CI)                                   0.67 (0.41 - 1.09)   0.94 (0.58 - 1.52)
Complete Response (CR)                 60 (44.8%)         47 (35.1%)           57 (43.2%)    
  95% CI (Wald, with correction)     (35.98, 53.57)     (26.62, 43.53)       (34.35, 52.01)  
Partial Response (PR)                  40 (29.9%)         37 (27.6%)           24 (18.2%)    
  95% CI (Wald, with correction)     (21.73, 37.97)     (19.67, 35.55)       (11.22, 25.14)  
Stable Disease (SD)                     9 (6.7%)          22 (16.4%)           13 (9.8%)     
  95% CI (Wald, with correction)     (2.11, 11.33)      (9.77, 23.06)        (4.39, 15.31)   
Progressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    
  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  
Not Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     
  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)

Experimental use!

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Similarly to in the previous tab, redefinition or relabeling of the result is a pre-processing step and the original table layout can be reused.

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Code

anl <- anl_adsl %>%
  left_join(anl_adrs, by = c("STUDYID", "USUBJID")) %>%
  mutate(rsp_lab = as.character(d_onco_rsp_label(AVALC))) %>%
  mutate(
    rsp_lab = case_when(
      rsp_lab == "Complete Response (CR)" ~ "No Progression",
      rsp_lab == "Partial Response (PR)" ~ "No Progression",
      rsp_lab == "Stable Disease (SD)" ~ "No Progression",
      TRUE ~ rsp_lab
    )
  ) %>%
  mutate(is_rsp = rsp_lab %in% "No Progression") %>%
  mutate(ARM = relevel(ARM, ref = "A: Drug X")) %>%
  var_relabel(ARM = "Description of Planned Arm")

result <- build_table(lyt = lyt_01, df = anl)
result

                                       A: Drug X          B: Placebo         C: Combination  
                                        (N=134)            (N=134)              (N=132)      
—————————————————————————————————————————————————————————————————————————————————————————————
Responders                            109 (81.3%)        106 (79.1%)           94 (71.2%)    
95% CI (Wald, with correction)        (74.4, 88.3)       (71.8, 86.4)         (63.1, 79.3)   
Unstratified Analysis                                                                        
  Difference in Response rate (%)                            -2.2                -10.1       
    95% CI (Wald, with correction)                       (-12.5, 8.0)         (-21.0, 0.8)   
  p-value (Chi-Squared Test)                                0.6455               0.0520      
Odds Ratio (95% CI)                                   0.87 (0.48 - 1.59)   0.57 (0.32 - 1.01)
Progressive Disease (PD)               24 (17.9%)         16 (11.9%)           33 (25.0%)    
  95% CI (Wald, with correction)     (11.05, 24.78)     (6.08, 17.80)        (17.23, 32.77)  
No Progression                        109 (81.3%)        106 (79.1%)           94 (71.2%)    
  95% CI (Wald, with correction)     (74.37, 88.31)     (71.85, 86.36)       (63.11, 79.31)  
Not Evaluable (NE)                      1 (0.7%)          12 (9.0%)             5 (3.8%)     
  95% CI (Wald, with correction)      (0.00, 2.58)      (3.75, 14.16)         (0.15, 7.42)

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(dplyr)
library(tern)

adsl <- random.cdisc.data::cadsl
adrs <- random.cdisc.data::cadrs

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

anl_adrs <- adrs %>%
  filter(PARAMCD == "INVET") %>%
  select(STUDYID, USUBJID, PARAMCD, AVISIT, AVALC)
anl_adsl <- adsl %>%
  select(STUDYID, USUBJID, ARM, STRATA1)

Code

library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl
  ADRS <- random.cdisc.data::cadrs

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

  ADSL <- ADSL %>%
    mutate(Dum_ARM = factor(rep("Single ARM", nrow(.))))
  ADRS <- ADRS %>%
    mutate(Dum_ARM = factor(rep("Single ARM", nrow(.))))
})
datanames <- c("ADSL", "ADRS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADRS <- data[["ADRS"]]
arm_ref_comp <- list(
  ACTARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  ),
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  )
)

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_binary_outcome(
      label = "Responders",
      dataname = "ADRS",
      paramcd = choices_selected(
        choices = value_choices(ADRS, "PARAMCD", "PARAM"),
        selected = "BESRSPI"
      ),
      arm_var = choices_selected(
        choices = variable_choices(ADRS, c("ARM", "ARMCD", "ACTARMCD", "Dum_ARM")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      strata_var = choices_selected(
        choices = variable_choices(ADRS, c("SEX", "BMRKR2")),
        select = NULL
      ),
      rsp_table = TRUE
    )
  )
)

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
  ADRS <- random.cdisc.data::cadrs

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

  ADSL <- ADSL %>%
    mutate(Dum_ARM = factor(rep("Single ARM", nrow(.))))
  ADRS <- ADRS %>%
    mutate(Dum_ARM = factor(rep("Single ARM", nrow(.))))
})
datanames <- c("ADSL", "ADRS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADRS <- data[["ADRS"]]
arm_ref_comp <- list(
  ACTARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  ),
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  )
)

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_binary_outcome(
      label = "Responders",
      dataname = "ADRS",
      paramcd = choices_selected(
        choices = value_choices(ADRS, "PARAMCD", "PARAM"),
        selected = "BESRSPI"
      ),
      arm_var = choices_selected(
        choices = variable_choices(ADRS, c("ARM", "ARMCD", "ACTARMCD", "Dum_ARM")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      strata_var = choices_selected(
        choices = variable_choices(ADRS, c("SEX", "BMRKR2")),
        select = NULL
      ),
      rsp_table = TRUE
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 17:42:01 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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Download the .lock file and use renv::restore() on it to recreate environment used to generate this website.

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