LBT11

Time to First Increase in Liver Laboratory Test Result Meeting Hy’s Law Laboratory Critieria (with Respect to ULN)

---

Output

Time-To-Event Summary

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
  count_occurrences(vars = "event_grp") %>%
  surv_time(
    vars = "AVAL",
    var_labels = paste0("Time to 1st LLT Event (", levels(anl$AVALU), ")"),
    is_event = "is_event",
    table_names = "time_to_event"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = "Unstratified Analysis",
    control = control_coxph(pval_method = "log-rank"),
    table_names = "coxph_unstratified"
  )

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

                                      ARM A              ARM B              ARM C      
                                     (N=134)            (N=134)            (N=132)     
———————————————————————————————————————————————————————————————————————————————————————
Patients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    
Patients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   
Time to 1st LLT Event (DAYS)                                                           
  Median                                NA                 NA               728.0      
    95% CI                              NA            (724.0, NA)             NA       
  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  
  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}
Unstratified Analysis                                                                  
  p-value (log-rank)                                     0.0295             0.1687     
  Hazard Ratio                                            0.43               0.58      
  95% CI                                              (0.19, 0.94)       (0.26, 1.27)  
———————————————————————————————————————————————————————————————————————————————————————

{1} - Censored observations: range minimum & maximum
{2} - Censored observation: range minimum
———————————————————————————————————————————————————————————————————————————————————————

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Experimental use!

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lyt <- basic_table(show_colcounts = TRUE) %>%

split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%

count_occurrences(vars = "event_grp") %>%

surv_time(

vars = "AVAL",

var_labels = paste0("Time to 1st LLT Event (", levels(anl$AVALU), ")"),

is_event = "is_event",

table_names = "time_to_event"

) %>%

coxph_pairwise(

vars = "AVAL",

is_event = "is_event",

var_labels = "Unstratified Analysis",

control = control_coxph(pval_method = "log-rank"),

table_names = "coxph_unstratified"

)

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

result

_webr_editor_2 = Object {code: "lyt <- basic_table(show_colcounts = TRUE) %>%\n sp…(lyt, df = anl, alt_counts_df = adsl_safl)\nresult", options: Object, indicator: Ke}

Time-To-Event Summary Selecting Sections to Display

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
  count_occurrences(vars = "event_grp") %>%
  surv_time(
    vars = "AVAL",
    var_labels = paste0("Time to 1st LLT Event (", levels(anl$AVALU), ")"),
    is_event = "is_event",
    table_names = "time_to_event"
  )

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

                                      ARM A              ARM B              ARM C      
                                     (N=134)            (N=134)            (N=132)     
———————————————————————————————————————————————————————————————————————————————————————
Patients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    
Patients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   
Time to 1st LLT Event (DAYS)                                                           
  Median                                NA                 NA               728.0      
    95% CI                              NA            (724.0, NA)             NA       
  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  
  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}
———————————————————————————————————————————————————————————————————————————————————————

{1} - Censored observations: range minimum & maximum
{2} - Censored observation: range minimum
———————————————————————————————————————————————————————————————————————————————————————

Try this using WebR

Experimental use!

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lyt <- basic_table(show_colcounts = TRUE) %>%

split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%

count_occurrences(vars = "event_grp") %>%

surv_time(

vars = "AVAL",

var_labels = paste0("Time to 1st LLT Event (", levels(anl$AVALU), ")"),

is_event = "is_event",

table_names = "time_to_event"

)

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

result

_webr_editor_3 = Object {code: "lyt <- basic_table(show_colcounts = TRUE) %>%\n sp…(lyt, df = anl, alt_counts_df = adsl_safl)\nresult", options: Object, indicator: Ke}

Time-To-Event Summary with Stratified Analysis

Preview

strata <- c("RACE", "SEX")

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%
  count_occurrences(vars = "event_grp") %>%
  surv_time(
    vars = "AVAL",
    var_labels = paste0("Time to 1st LLT Event (", levels(anl$AVALU), ")"),
    is_event = "is_event",
    table_names = "time_to_event"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = "Unstratified Analysis",
    control = control_coxph(pval_method = "log-rank"),
    table_names = "coxph_unstratified"
  ) %>%
  coxph_pairwise(
    vars = "AVAL",
    is_event = "is_event",
    var_labels = paste0("Stratified By: ", paste(strata, collapse = ", ")),
    strata = strata,
    table_names = "coxph_stratified"
  )

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

                                      ARM A              ARM B              ARM C      
                                     (N=134)            (N=134)            (N=132)     
———————————————————————————————————————————————————————————————————————————————————————
Patients with LLT event (%)         20 (14.9%)          9 (6.7%)           9 (6.8%)    
Patients without LLT event (%)     114 (85.1%)        125 (93.3%)        123 (93.2%)   
Time to 1st LLT Event (DAYS)                                                           
  Median                                NA                 NA               728.0      
    95% CI                              NA            (724.0, NA)             NA       
  25% and 75%-ile                   618.0, NA          724.0, NA         728.0, 728.0  
  Range                          1.0 to 731.0 {1}   1.0 to 728.0 {1}   1.0 to 728.0 {2}
Unstratified Analysis                                                                  
  p-value (log-rank)                                     0.0295             0.1687     
  Hazard Ratio                                            0.43               0.58      
  95% CI                                              (0.19, 0.94)       (0.26, 1.27)  
Stratified By: RACE, SEX                                                               
  p-value (log-rank)                                     0.0858             0.1605     
  Hazard Ratio                                            0.51               0.57      
  95% CI                                              (0.23, 1.12)       (0.26, 1.26)  
———————————————————————————————————————————————————————————————————————————————————————

{1} - Censored observations: range minimum & maximum
{2} - Censored observation: range minimum
———————————————————————————————————————————————————————————————————————————————————————

Try this using WebR

Experimental use!

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strata <- c("RACE", "SEX")

lyt <- basic_table(show_colcounts = TRUE) %>%

split_cols_by(var = "ARMCD", ref_group = "ARM A") %>%

count_occurrences(vars = "event_grp") %>%

surv_time(

vars = "AVAL",

var_labels = paste0("Time to 1st LLT Event (", levels(anl$AVALU), ")"),

is_event = "is_event",

table_names = "time_to_event"

) %>%

coxph_pairwise(

vars = "AVAL",

is_event = "is_event",

var_labels = "Unstratified Analysis",

control = control_coxph(pval_method = "log-rank"),

table_names = "coxph_unstratified"

) %>%

coxph_pairwise(

vars = "AVAL",

is_event = "is_event",

var_labels = paste0("Stratified By: ", paste(strata, collapse = ", ")),

strata = strata,

table_names = "coxph_stratified"

)

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

result

_webr_editor_4 = Object {code: "strata <- c(\"RACE\", \"SEX\")\n\nlyt <- basic_table(sho…(lyt, df = anl, alt_counts_df = adsl_safl)\nresult", options: Object, indicator: Ke}

Data Setup

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adaette <- random.cdisc.data::cadaette

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

adsl_safl <- dplyr::filter(adsl, .data$SAFFL == "Y")

anl <- adaette %>%
  dplyr::filter(
    SAFFL == "Y",
    PARAMCD == "HYSTTEUL"
  ) %>%
  dplyr::mutate(
    AVAL = .data$AVAL * dplyr::case_when( # convert to days, if possible
      .data$AVALU == "WEEKS" ~ 7,
      .data$AVALU == "MONTHS" ~ 30.4375,
      .data$AVALU == "YEARS" ~ 365,
      TRUE ~ 1
    ),
    AVALU = factor(dplyr::case_when(
      .data$AVALU %in% c("WEEKS", "MONTHS", "YEARS") ~ factor("DAYS"),
      TRUE ~ .data$AVALU
    ), levels = "DAYS"),
    is_event = CNSR == 0, # this will be a LLT event
    event_grp = factor(
      dplyr::case_when(
        CNSR == 0 ~ "Patients with LLT event (%)",
        CNSR == 1 ~ "Patients without LLT event (%)"
      ),
      levels = c(
        "Patients with LLT event (%)",
        "Patients without LLT event (%)"
      )
    )
  )

teal App

Preview

library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl
  ADAETTE <- random.cdisc.data::cadaette

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

  ADSL <- filter(ADSL, .data$SAFFL == "Y")
  ADAETTE <- filter(ADAETTE, .data$SAFFL == "Y")
})
datanames <- c("ADSL", "ADAETTE")
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"]]
ADAETTE <- data[["ADAETTE"]]
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 LLT Event Table",
      dataname = "ADAETTE",
      arm_var = choices_selected(
        variable_choices(data[["ADSL"]], c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(data[["ADAETTE"]], "PARAMCD", "PARAM"),
        "HYSTTEUL"
      ),
      strata_var = choices_selected(
        variable_choices(data[["ADSL"]], c("RACE", "SEX")),
        c("RACE", "SEX")
      ),
      time_points = choices_selected(0, NULL),
      event_desc_var = choices_selected("EVNTDESC", "EVNTDESC", fixed = TRUE)
    )
  )
)

shinyApp(app$ui, app$server)

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Reproducibility

Timestamp

[1] "2025-04-09 17:52:28 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.2 (2024-10-31)
 os       Ubuntu 24.04.1 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-04-09
 pandoc   3.6.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.6.43 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version     date (UTC) lib source
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 brio                    1.1.5       2024-04-24 [1] RSPM
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 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 4.4.2)
 teal                  * 0.16.0.9003 2025-04-09 [1] https://p~
 teal.code             * 0.6.1.9000  2025-04-09 [1] https://p~
 teal.data             * 0.7.0.9002  2025-04-09 [1] https://p~
 teal.logger             0.3.2.9000  2025-04-09 [1] https://p~
 teal.modules.clinical * 0.10.0.9003 2025-04-09 [1] https://p~
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 teal.slice            * 0.6.0.9002  2025-04-09 [1] https://p~
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library
 [3] /github/home/R/x86_64-pc-linux-gnu-library/4.4
 * ── Packages attached to the search path.

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

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