TTET01

Time-To-Event Summary

---

Output

Standard Table

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(
    var = "ARM", ref_group = "A: Drug X"
  ) %>%
  analyze_vars(
    vars = "is_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients with event (%)")
  ) %>%
  split_rows_by(
    "EVNT1",
    split_label = "Earliest contributing event",
    split_fun = keep_split_levels("Patients with event (%)"),
    label_pos = "visible",
    child_labels = "hidden",
    indent_mod = 1L,
  ) %>%
  analyze("EVNTDESC") %>%
  analyze_vars(
    vars = "is_not_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients without event (%)"),
    nested = FALSE,
    show_labels = "hidden"
  ) %>%
  surv_time(
    vars = "AVAL",
    var_labels = "Time to Event (Months)",
    is_event = "is_event",
    table_names = "time_to_event"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = c("Unstratified Analysis"),
    control = control_coxph(pval_method = "log-rank"),
    table_names = "coxph_unstratified"
  ) %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    time_point = c(6, 12),
    is_event = "is_event",
    method = "both",
    control = control_surv_timepoint()
  )

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

result

                                     A: Drug X        B: Placebo      C: Combination 
                                      (N=134)           (N=134)           (N=132)    
—————————————————————————————————————————————————————————————————————————————————————
Patients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   
  Earliest contributing event                                                        
    Death                               58                58                69       
Patients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   
Time to Event (Months)                                                               
  Median                                NA                NA                9.4      
    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   
  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    
  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}
Unstratified Analysis                                                                
  p-value (log-rank)                                    0.9998            0.1541     
  Hazard Ratio                                           1.00              1.29      
  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  
6 Months                                                                             
  Patients remaining at risk            97                97                90       
  Event Free Rate (%)                  72.39             72.39             68.18     
  95% CI                          (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) 
  Difference in Event Free Rate                          0.00              -4.21     
    95% CI                                          (-10.71, 10.71)   (-15.18, 6.77) 
    p-value (Z-test)                                    1.0000            0.4525     
12 Months                                                                            
  Patients remaining at risk            49                48                37       
  Event Free Rate (%)                  56.72             56.72             47.73     
  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) 
  Difference in Event Free Rate                          0.00              -8.99     
    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) 
    p-value (Z-test)                                    1.0000            0.1406     
—————————————————————————————————————————————————————————————————————————————————————

{1} - Censored observation: range maximum
—————————————————————————————————————————————————————————————————————————————————————

Try this using WebR

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.

Table Selecting Sections to Display

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM", ref_group = "A: Drug X") %>%
  analyze_vars(
    vars = "is_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients with event (%)")
  ) %>%
  analyze_vars(
    "is_not_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients without event (%)"),
    nested = FALSE,
    show_labels = "hidden"
  ) %>%
  surv_time(
    vars = "AVAL",
    var_labels = "Time to Event (Months)",
    is_event = "is_event",
    table_names = "time_to_event"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = c("Unstratified Analysis"),
    control = control_coxph(pval_method = "log-rank"),
    table_names = "coxph_unstratified"
  ) %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    is_event = "is_event",
    method = "surv",
    time_point = c(6, 12)
  )

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

                                  A: Drug X        B: Placebo      C: Combination 
                                   (N=134)           (N=134)           (N=132)    
——————————————————————————————————————————————————————————————————————————————————
Patients with event (%)          58 (43.3%)        58 (43.3%)        69 (52.3%)   
Patients without event (%)       76 (56.7%)        76 (56.7%)        63 (47.7%)   
Time to Event (Months)                                                            
  Median                             NA                NA                9.4      
    95% CI                        (9.3, NA)         (9.4, NA)         (7.6, NA)   
  25% and 75%-ile                  5.6, NA           5.6, NA           5.0, NA    
  Range                        0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}
Unstratified Analysis                                                             
  p-value (log-rank)                                 0.9998            0.1541     
  Hazard Ratio                                        1.00              1.29      
  95% CI                                          (0.69, 1.44)      (0.91, 1.83)  
6 Months                                                                          
  Patients remaining at risk         97                97                90       
  Event Free Rate (%)               72.39             72.39             68.18     
  95% CI                       (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) 
12 Months                                                                         
  Patients remaining at risk         49                48                37       
  Event Free Rate (%)               56.72             56.72             47.73     
  95% CI                       (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) 
——————————————————————————————————————————————————————————————————————————————————

{1} - Censored observation: range maximum
——————————————————————————————————————————————————————————————————————————————————

Try this using WebR

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.

Table Modifying Analysis Details like Conf. Type, Ties, Alpha Level

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM", ref_group = "A: Drug X") %>%
  analyze_vars(
    vars = "is_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients with event (%)")
  ) %>%
  split_rows_by(
    "EVNT1",
    split_label = "Earliest contributing event",
    split_fun = keep_split_levels("Patients with event (%)"),
    label_pos = "visible",
    child_labels = "hidden",
    indent_mod = 1L,
  ) %>%
  analyze("EVNTDESC") %>%
  analyze_vars(
    vars = "is_not_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients without event (%)"),
    nested = FALSE,
    show_labels = "hidden"
  ) %>%
  surv_time(
    vars = "AVAL",
    var_labels = "Time to Event (Months)",
    is_event = "is_event",
    control = control_surv_time(conf_level = 0.9, conf_type = "log-log"),
    table_names = "time_to_event"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = c("Unstratified Analysis"),
    control = control_coxph(
      pval_method = "log-rank",
      conf_level = 0.95,
      ties = "efron"
    ),
    table_names = "coxph_unstratified"
  ) %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    is_event = "is_event",
    time_point = 12,
    control = control_surv_timepoint(conf_level = 0.9, conf_type = "log-log"),
    table_names_suffix = "_log_log"
  ) %>%
  surv_timepoint(
    vars = "AVAL",
    show_labels = "hidden",
    is_event = "is_event",
    time_point = 12,
    method = "surv_diff",
    control = control_surv_timepoint(conf_level = 0.975),
    table_names_suffix = "_975_pct"
  )

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

result

                                   A: Drug X        B: Placebo      C: Combination 
                                    (N=134)           (N=134)           (N=132)    
———————————————————————————————————————————————————————————————————————————————————
Patients with event (%)           58 (43.3%)        58 (43.3%)        69 (52.3%)   
  Earliest contributing event                                                      
    Death                             58                58                69       
Patients without event (%)        76 (56.7%)        76 (56.7%)        63 (47.7%)   
Time to Event (Months)                                                             
  Median                              NA                NA                9.4      
    90% CI                         (9.6, NA)         (9.6, NA)         (7.7, NA)   
  25% and 75%-ile                   5.6, NA           5.6, NA           5.0, NA    
  Range                         0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}
Unstratified Analysis                                                              
  p-value (log-rank)                                  0.9998            0.1541     
  Hazard Ratio                                         1.00              1.29      
  95% CI                                           (0.69, 1.44)      (0.91, 1.83)  
12 Months                                                                          
  Patients remaining at risk          49                48                37       
  Event Free Rate (%)                56.72             56.72             47.73     
  90% CI                        (49.37, 63.41)    (49.37, 63.41)    (40.42, 54.66) 
Difference in Event Free Rate                          0.00              -8.99     
  97.5% CI                                        (-13.57, 13.57)   (-22.66, 4.69) 
  p-value (Z-test)                                    1.0000            0.1406     
———————————————————————————————————————————————————————————————————————————————————

{1} - Censored observation: range maximum
———————————————————————————————————————————————————————————————————————————————————

Try this using WebR

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.

Table with Stratified Analysis

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM", ref_group = "A: Drug X") %>%
  analyze_vars(
    vars = "is_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients with event (%)")
  ) %>%
  split_rows_by(
    "EVNT1",
    split_label = "Earliest contributing event",
    split_fun = keep_split_levels("Patients with event (%)"),
    label_pos = "visible",
    child_labels = "hidden",
    indent_mod = 1L,
  ) %>%
  analyze("EVNTDESC") %>%
  analyze_vars(
    vars = "is_not_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients without event (%)"),
    nested = FALSE,
    show_labels = "hidden"
  ) %>%
  surv_time(
    vars = "AVAL",
    var_labels = "Time to Event (Months)",
    is_event = "is_event",
    table_names = "time_to_event"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = "Unstratified Analysis",
    table_names = "coxph_unstratified"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = "Stratified Analysis",
    strata = "SEX",
    table_names = "coxph_stratified"
  ) %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    is_event = "is_event",
    method = "both",
    time_point = 12
  )

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

result

                                     A: Drug X        B: Placebo      C: Combination 
                                      (N=134)           (N=134)           (N=132)    
—————————————————————————————————————————————————————————————————————————————————————
Patients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   
  Earliest contributing event                                                        
    Death                               58                58                69       
Patients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   
Time to Event (Months)                                                               
  Median                                NA                NA                9.4      
    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   
  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    
  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}
Unstratified Analysis                                                                
  p-value (log-rank)                                    0.9998            0.1541     
  Hazard Ratio                                           1.00              1.29      
  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  
Stratified Analysis                                                                  
  p-value (log-rank)                                    0.9978            0.1733     
  Hazard Ratio                                           1.00              1.27      
  95% CI                                             (0.69, 1.44)      (0.90, 1.81)  
12 Months                                                                            
  Patients remaining at risk            49                48                37       
  Event Free Rate (%)                  56.72             56.72             47.73     
  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) 
  Difference in Event Free Rate                          0.00              -8.99     
    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) 
    p-value (Z-test)                                    1.0000            0.1406     
—————————————————————————————————————————————————————————————————————————————————————

{1} - Censored observation: range maximum
—————————————————————————————————————————————————————————————————————————————————————

Try this using WebR

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.

Table Modifying Time Point for the “XX Months” Analysis

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM", ref_group = "A: Drug X") %>%
  analyze_vars(
    vars = "is_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients with event (%)")
  ) %>%
  split_rows_by(
    "EVNT1",
    split_label = "Earliest contributing event",
    split_fun = keep_split_levels("Patients with event (%)"),
    label_pos = "visible",
    child_labels = "hidden",
    indent_mod = 1L,
  ) %>%
  analyze("EVNTDESC") %>%
  analyze_vars(
    vars = "is_not_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients without event (%)"),
    nested = FALSE,
    show_labels = "hidden"
  ) %>%
  surv_time(
    vars = "AVAL",
    var_labels = "Time to Event (Months)",
    is_event = "is_event",
    table_names = "time_to_event"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = c("Unstratified Analysis"),
    control = control_coxph(pval_method = "log-rank"),
    table_names = "coxph_unstratified"
  ) %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    is_event = "is_event",
    time_point = 6,
    method = "both"
  )

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

result

                                     A: Drug X        B: Placebo      C: Combination 
                                      (N=134)           (N=134)           (N=132)    
—————————————————————————————————————————————————————————————————————————————————————
Patients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   
  Earliest contributing event                                                        
    Death                               58                58                69       
Patients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   
Time to Event (Months)                                                               
  Median                                NA                NA                9.4      
    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   
  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    
  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}
Unstratified Analysis                                                                
  p-value (log-rank)                                    0.9998            0.1541     
  Hazard Ratio                                           1.00              1.29      
  95% CI                                             (0.69, 1.44)      (0.91, 1.83)  
6 Months                                                                             
  Patients remaining at risk            97                97                90       
  Event Free Rate (%)                  72.39             72.39             68.18     
  95% CI                          (64.82, 79.96)    (64.82, 79.96)    (60.24, 76.13) 
  Difference in Event Free Rate                          0.00              -4.21     
    95% CI                                          (-10.71, 10.71)   (-15.18, 6.77) 
    p-value (Z-test)                                    1.0000            0.4525     
—————————————————————————————————————————————————————————————————————————————————————

{1} - Censored observation: range maximum
—————————————————————————————————————————————————————————————————————————————————————

Try this using WebR

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.

Table Requesting > 1 p-value

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM", ref_group = "A: Drug X") %>%
  analyze_vars(
    vars = "is_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients with event (%)")
  ) %>%
  split_rows_by(
    "EVNT1",
    split_label = "Earliest contributing event",
    split_fun = keep_split_levels("Patients with event (%)"),
    label_pos = "visible",
    child_labels = "hidden",
    indent_mod = 1L,
  ) %>%
  analyze("EVNTDESC") %>%
  analyze_vars(
    vars = "is_not_event",
    .stats = "count_fraction",
    .labels = c(count_fraction = "Patients without event (%)"),
    nested = FALSE,
    show_labels = "hidden"
  ) %>%
  surv_time(
    vars = "AVAL",
    var_labels = "Time to Event (Months)",
    is_event = "is_event",
    table_names = "time_to_event"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = c("Unstratified Analysis"),
    control = control_coxph(pval_method = "log-rank"),
    .stats = "pvalue",
    table_names = "coxph_unstratified"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    show_labels = "hidden",
    control = control_coxph(pval_method = "wald"),
    .stats = "pvalue",
    .indent_mods = c(pvalue = 1L),
    table_names = "coxph_wald_pvalue"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    show_labels = "hidden",
    control = control_coxph(pval_method = "likelihood"),
    .indent_mods = c(pvalue = 1L, hr = 2L, hr_ci = 3L),
    table_names = "coxph_likelihood_pvalue"
  ) %>%
  surv_timepoint(
    vars = "AVAL",
    var_labels = "Months",
    is_event = "is_event",
    time_point = 12,
    method = "both"
  )

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

result

                                     A: Drug X        B: Placebo      C: Combination 
                                      (N=134)           (N=134)           (N=132)    
—————————————————————————————————————————————————————————————————————————————————————
Patients with event (%)             58 (43.3%)        58 (43.3%)        69 (52.3%)   
  Earliest contributing event                                                        
    Death                               58                58                69       
Patients without event (%)          76 (56.7%)        76 (56.7%)        63 (47.7%)   
Time to Event (Months)                                                               
  Median                                NA                NA                9.4      
    95% CI                           (9.3, NA)         (9.4, NA)         (7.6, NA)   
  25% and 75%-ile                     5.6, NA           5.6, NA           5.0, NA    
  Range                           0.5 to 16.4 {1}   0.9 to 16.3 {1}   0.5 to 16.3 {1}
Unstratified Analysis                                                                
  p-value (log-rank)                                    0.9998            0.1541     
  p-value (wald)                                        0.9998            0.1552     
  p-value (likelihood)                                  0.9998            0.1543     
    Hazard Ratio                                         1.00              1.29      
      95% CI                                         (0.69, 1.44)      (0.91, 1.83)  
12 Months                                                                            
  Patients remaining at risk            49                48                37       
  Event Free Rate (%)                  56.72             56.72             47.73     
  95% CI                          (48.33, 65.11)    (48.33, 65.11)    (39.21, 56.25) 
  Difference in Event Free Rate                          0.00              -8.99     
    95% CI                                          (-11.86, 11.86)   (-20.95, 2.97) 
    p-value (Z-test)                                    1.0000            0.1406     
—————————————————————————————————————————————————————————————————————————————————————

{1} - Censored observation: range maximum
—————————————————————————————————————————————————————————————————————————————————————

Try this using WebR

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.

Data Setup

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adtte <- random.cdisc.data::cadtte

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

adtte_f <- adtte %>%
  dplyr::filter(PARAMCD == "OS") %>%
  dplyr::mutate(
    AVAL = day2month(AVAL),
    is_event = CNSR == 0,
    is_not_event = CNSR == 1,
    EVNT1 = factor(
      case_when(
        is_event == TRUE ~ "Patients with event (%)",
        is_event == FALSE ~ "Patients without event (%)"
      ),
      levels = c("Patients with event (%)", "Patients without event (%)")
    ),
    EVNTDESC = factor(EVNTDESC)
  )

teal App

Preview

library(teal.modules.clinical)

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

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADTTE <- df_explicit_na(ADTTE)
})
datanames <- c("ADSL", "ADTTE")
datanames(data) <- datanames

Warning: `datanames<-()` was deprecated in teal.data 0.7.0.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data

join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]
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_tte(
      label = "Time To Event Table",
      dataname = "ADTTE",
      arm_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(ADTTE, "PARAMCD", "PARAM"),
        "OS"
      ),
      strata_var = choices_selected(
        variable_choices(ADSL, c("SEX", "BMRKR2")),
        "SEX"
      ),
      time_points = choices_selected(c(6, 8), 6),
      event_desc_var = choices_selected(
        variable_choices(ADTTE, "EVNTDESC"),
        "EVNTDESC",
        fixed = TRUE
      )
    )
  )
)

shinyApp(app$ui, app$server)

Try this using shinylive

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.

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

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

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

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

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADTTE <- df_explicit_na(ADTTE)
})
datanames <- c("ADSL", "ADTTE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADTTE <- data[["ADTTE"]]
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_tte(
      label = "Time To Event Table",
      dataname = "ADTTE",
      arm_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(ADTTE, "PARAMCD", "PARAM"),
        "OS"
      ),
      strata_var = choices_selected(
        variable_choices(ADSL, c("SEX", "BMRKR2")),
        "SEX"
      ),
      time_points = choices_selected(c(6, 8), 6),
      event_desc_var = choices_selected(
        variable_choices(ADTTE, "EVNTDESC"),
        "EVNTDESC",
        fixed = TRUE
      )
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2025-07-09 17:42:39 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11)
 os       Ubuntu 24.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-07-09
 pandoc   3.7.0.2 @ /usr/bin/ (via rmarkdown)
 quarto   1.7.32 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
 backports               1.5.0    2024-05-23 [1] RSPM
 brio                    1.1.5    2024-04-24 [1] RSPM
 broom                   1.0.8    2025-03-28 [1] RSPM
 bslib                   0.9.0    2025-01-30 [1] RSPM
 cachem                  1.1.0    2024-05-16 [1] RSPM
 callr                   3.7.6    2024-03-25 [1] RSPM
 checkmate               2.3.2    2024-07-29 [1] RSPM
 chromote                0.5.1    2025-04-24 [1] RSPM
 cli                     3.6.5    2025-04-23 [1] RSPM
 coda                    0.19-4.1 2024-01-31 [1] CRAN (R 4.5.0)
 codetools               0.2-20   2024-03-31 [2] CRAN (R 4.5.0)
 curl                    6.4.0    2025-06-22 [1] RSPM
 dichromat               2.0-0.1  2022-05-02 [1] CRAN (R 4.5.0)
 digest                  0.6.37   2024-08-19 [1] RSPM
 dplyr                 * 1.1.4    2023-11-17 [1] RSPM
 emmeans                 1.11.1   2025-05-04 [1] RSPM
 estimability            1.5.1    2024-05-12 [1] RSPM
 evaluate                1.0.4    2025-06-18 [1] RSPM
 farver                  2.1.2    2024-05-13 [1] RSPM
 fastmap                 1.2.0    2024-05-15 [1] RSPM
 fontawesome             0.5.3    2024-11-16 [1] RSPM
 forcats                 1.0.0    2023-01-29 [1] RSPM
 formatR                 1.14     2023-01-17 [1] CRAN (R 4.5.0)
 formatters            * 0.5.11   2025-04-09 [1] RSPM
 geepack                 1.3.12   2024-09-23 [1] RSPM
 generics                0.1.4    2025-05-09 [1] RSPM
 ggplot2                 3.5.2    2025-04-09 [1] RSPM
 glue                    1.8.0    2024-09-30 [1] RSPM
 gtable                  0.3.6    2024-10-25 [1] RSPM
 htmltools               0.5.8.1  2024-04-04 [1] RSPM
 htmlwidgets             1.6.4    2023-12-06 [1] RSPM
 httpuv                  1.6.16   2025-04-16 [1] RSPM
 jquerylib               0.1.4    2021-04-26 [1] RSPM
 jsonlite                2.0.0    2025-03-27 [1] RSPM
 knitr                   1.50     2025-03-16 [1] RSPM
 later                   1.4.2    2025-04-08 [1] RSPM
 lattice                 0.22-7   2025-04-02 [2] CRAN (R 4.5.0)
 lifecycle               1.0.4    2023-11-07 [1] RSPM
 logger                  0.4.0    2024-10-22 [1] RSPM
 magrittr              * 2.0.3    2022-03-30 [1] RSPM
 MASS                    7.3-65   2025-02-28 [2] CRAN (R 4.5.0)
 Matrix                  1.7-3    2025-03-11 [1] CRAN (R 4.5.0)
 memoise                 2.0.1    2021-11-26 [1] RSPM
 mime                    0.13     2025-03-17 [1] RSPM
 multcomp                1.4-28   2025-01-29 [1] RSPM
 mvtnorm                 1.3-3    2025-01-10 [1] RSPM
 nestcolor               0.1.3    2025-01-21 [1] RSPM
 nlme                    3.1-168  2025-03-31 [2] CRAN (R 4.5.0)
 pillar                  1.11.0   2025-07-04 [1] RSPM
 pkgcache                2.2.4    2025-05-26 [1] RSPM
 pkgconfig               2.0.3    2019-09-22 [1] RSPM
 processx                3.8.6    2025-02-21 [1] RSPM
 promises                1.3.3    2025-05-29 [1] RSPM
 ps                      1.9.1    2025-04-12 [1] RSPM
 purrr                   1.0.4    2025-02-05 [1] RSPM
 R6                      2.6.1    2025-02-15 [1] RSPM
 random.cdisc.data       0.3.16   2024-10-10 [1] RSPM
 rbibutils               2.3      2024-10-04 [1] RSPM
 RColorBrewer            1.1-3    2022-04-03 [1] RSPM
 Rcpp                    1.1.0    2025-07-02 [1] RSPM
 Rdpack                  2.6.4    2025-04-09 [1] RSPM
 rlang                   1.1.6    2025-04-11 [1] RSPM
 rmarkdown               2.29     2024-11-04 [1] RSPM
 rtables               * 0.6.13   2025-06-19 [1] RSPM
 sandwich                3.1-1    2024-09-15 [1] RSPM
 sass                    0.4.10   2025-04-11 [1] RSPM
 scales                  1.4.0    2025-04-24 [1] RSPM
 sessioninfo             1.2.3    2025-02-05 [1] any (@1.2.3)
 shiny                 * 1.11.1   2025-07-03 [1] RSPM
 shinycssloaders         1.1.0    2024-07-30 [1] RSPM
 shinyjs                 2.1.0    2021-12-23 [1] RSPM
 shinyvalidate           0.1.3    2023-10-04 [1] RSPM
 shinyWidgets            0.9.0    2025-02-21 [1] RSPM
 stringi                 1.8.7    2025-03-27 [1] RSPM
 stringr                 1.5.1    2023-11-14 [1] RSPM
 survival                3.8-3    2024-12-17 [2] CRAN (R 4.5.0)
 teal                  * 0.16.0   2025-02-23 [1] RSPM
 teal.code             * 0.6.1    2025-02-14 [1] RSPM
 teal.data             * 0.7.0    2025-01-28 [1] RSPM
 teal.logger             0.4.0    2025-07-08 [1] RSPM
 teal.modules.clinical * 0.10.0   2025-02-28 [1] RSPM
 teal.reporter           0.4.0    2025-01-24 [1] RSPM
 teal.slice            * 0.6.0    2025-02-03 [1] RSPM
 teal.transform        * 0.6.0    2025-02-12 [1] RSPM
 teal.widgets            0.4.3    2025-01-31 [1] RSPM
 tern                  * 0.9.9    2025-06-20 [1] RSPM
 tern.gee                0.1.5    2024-08-23 [1] RSPM
 testthat                3.2.3    2025-01-13 [1] RSPM
 TH.data                 1.1-3    2025-01-17 [1] RSPM
 tibble                  3.3.0    2025-06-08 [1] RSPM
 tidyr                   1.3.1    2024-01-24 [1] RSPM
 tidyselect              1.2.1    2024-03-11 [1] RSPM
 vctrs                   0.6.5    2023-12-01 [1] RSPM
 webshot                 0.5.5    2023-06-26 [1] CRAN (R 4.5.0)
 webshot2                0.1.2    2025-04-23 [1] RSPM
 websocket               1.4.4    2025-04-10 [1] RSPM
 withr                   3.0.2    2024-10-28 [1] RSPM
 xfun                    0.52     2025-04-02 [1] RSPM
 xtable                  1.8-4    2019-04-21 [1] RSPM
 yaml                    2.3.10   2024-07-26 [1] RSPM
 zoo                     1.8-14   2025-04-10 [1] RSPM

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

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

.lock file

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

Download