LBT11_BL

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

Output

Preview
Try this using WebR

Code

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 (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   
Patients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   
Time to 1st LLT Event (DAYS)                                                           
  Median                                NA                 NA               721.0      
    95% CI                              NA            (681.0, NA)        (691.0, NA)   
  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    
  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}
Unstratified Analysis                                                                  
  p-value (log-rank)                                     0.5013             0.6413     
  Hazard Ratio                                            1.27               0.84      
  95% CI                                              (0.63, 2.56)       (0.40, 1.75)  
———————————————————————————————————————————————————————————————————————————————————————

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

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Preview
Try this using WebR

Code

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 (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   
Patients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   
Time to 1st LLT Event (DAYS)                                                           
  Median                                NA                 NA               721.0      
    95% CI                              NA            (681.0, NA)        (691.0, NA)   
  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    
  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}
———————————————————————————————————————————————————————————————————————————————————————

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

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Preview
Try this using WebR

Code

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 (%)         14 (10.4%)         18 (13.4%)         15 (11.4%)   
Patients without LLT event (%)     120 (89.6%)        116 (86.6%)        117 (88.6%)   
Time to 1st LLT Event (DAYS)                                                           
  Median                                NA                 NA               721.0      
    95% CI                              NA            (681.0, NA)        (691.0, NA)   
  25% and 75%-ile                   608.0, NA          558.0, NA          644.0, NA    
  Range                          1.0 to 728.0 {1}   1.0 to 726.0 {1}   1.0 to 724.0 {1}
Unstratified Analysis                                                                  
  p-value (log-rank)                                     0.5013             0.6413     
  Hazard Ratio                                            1.27               0.84      
  95% CI                                              (0.63, 2.56)       (0.40, 1.75)  
Stratified By: RACE, SEX                                                               
  p-value (log-rank)                                     0.3886             0.7970     
  Hazard Ratio                                            1.39               0.91      
  95% CI                                              (0.65, 2.97)       (0.43, 1.93)  
———————————————————————————————————————————————————————————————————————————————————————

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

Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Code

library(tern)
library(dplyr)

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 == "HYSTTEBL"
  ) %>%
  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 (%)"
      )
    )
  )

Code

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 0.6.1.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data

Code

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(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(ADAETTE, "PARAMCD", "PARAM"),
        "HYSTTEBL"
      ),
      strata_var = choices_selected(
        variable_choices(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)

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://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
  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
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(ADSL, c("ARM", "ARMCD", "ACTARMCD")),
        "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(ADAETTE, "PARAMCD", "PARAM"),
        "HYSTTEBL"
      ),
      strata_var = choices_selected(
        variable_choices(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)

Reproducibility

Timestamp

[1] "2024-11-20 17:57:26 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     2024-11-20
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ 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                   1.0.7       2024-09-26 [1] RSPM
 bslib                   0.8.0       2024-07-29 [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                0.3.1       2024-08-30 [1] RSPM
 cli                     3.6.3       2024-06-21 [1] RSPM
 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] RSPM
 curl                    6.0.1       2024-11-14 [1] RSPM
 digest                  0.6.37      2024-08-19 [1] RSPM
 dplyr                 * 1.1.4       2023-11-17 [1] RSPM
 emmeans                 1.10.5      2024-10-14 [1] RSPM
 estimability            1.5.1       2024-05-12 [1] RSPM
 evaluate                1.0.1       2024-10-10 [1] RSPM
 fansi                   1.0.6       2023-12-08 [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 4.4.1)
 formatters            * 0.5.9.9003  2024-11-20 [1] https://p~
 geepack                 1.3.12      2024-09-23 [1] RSPM
 generics                0.1.3       2022-07-05 [1] RSPM
 ggplot2                 3.5.1       2024-04-23 [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                  1.6.15      2024-03-26 [1] RSPM
 jquerylib               0.1.4       2021-04-26 [1] RSPM
 jsonlite                1.8.9       2024-09-20 [1] RSPM
 knitr                   1.49        2024-11-08 [1] RSPM
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 lattice                 0.22-6      2024-03-20 [2] CRAN (R 4.4.1)
 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                    7.3-61      2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-1       2024-10-18 [1] RSPM
 memoise                 2.0.1       2021-11-26 [1] RSPM
 mime                    0.12        2021-09-28 [1] RSPM
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 nlme                    3.1-166     2024-08-14 [2] CRAN (R 4.4.1)
 pillar                  1.9.0       2023-03-22 [1] RSPM
 pkgcache                2.2.3       2024-09-12 [1] RSPM
 pkgconfig               2.0.3       2019-09-22 [1] RSPM
 processx                3.8.4       2024-03-16 [1] RSPM
 promises                1.3.0       2024-04-05 [1] RSPM
 ps                      1.8.1       2024-10-28 [1] RSPM
 purrr                   1.0.2       2023-08-10 [1] RSPM
 R6                      2.5.1       2021-08-19 [1] RSPM
 random.cdisc.data       0.3.16.9001 2024-11-20 [1] https://p~
 rbibutils               2.3         2024-10-04 [1] RSPM
 Rcpp                    1.0.13-1    2024-11-02 [1] RSPM
 Rdpack                  2.6.2       2024-11-15 [1] RSPM
 rlang                   1.1.4       2024-06-04 [1] RSPM
 rmarkdown               2.29        2024-11-04 [1] RSPM
 rtables               * 0.6.10.9005 2024-11-20 [1] https://p~
 sandwich                3.1-1       2024-09-15 [1] CRAN (R 4.4.1)
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 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            0.8.7       2024-09-23 [1] RSPM
 stringi                 1.8.4       2024-05-06 [1] RSPM
 stringr                 1.5.1       2023-11-14 [1] RSPM
 survival                3.7-0       2024-06-05 [2] CRAN (R 4.4.1)
 teal                  * 0.15.2.9089 2024-11-20 [1] https://p~
 teal.code             * 0.5.0.9018  2024-11-20 [1] https://p~
 teal.data             * 0.6.0.9021  2024-11-20 [1] https://p~
 teal.logger             0.3.0.9002  2024-11-20 [1] https://p~
 teal.modules.clinical * 0.9.1.9031  2024-11-20 [1] https://p~
 teal.reporter           0.3.1.9016  2024-11-20 [1] https://p~
 teal.slice            * 0.5.1.9016  2024-11-20 [1] https://p~
 teal.transform        * 0.5.0.9016  2024-11-20 [1] https://p~
 teal.widgets            0.4.2.9022  2024-11-20 [1] https://p~
 tern                  * 0.9.6.9016  2024-11-20 [1] https://p~
 tern.gee                0.1.5.9004  2024-11-20 [1] https://p~
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 xtable                  1.8-4       2019-04-21 [1] RSPM
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

`.lock` file

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

Download

Output

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file