AOVT02

ANCOVA with Single End Point and Customized Table

This example will focus on data from a single visit “WEEK 1 DAY 8” and a single endpoint “FKSI-FWB”. Only evaluable populations will be analyzed.

Standard Table
Data Setup

Preview
Try this using WebR

Code

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARMCD", ref_group = "ARM A") %>%
  append_varlabels(adqs_single, "PARAM") %>%
  summarize_ancova(
    vars = "CHG",
    variables = list(arm = "ARMCD", covariates = NULL),
    conf_level = 0.95,
    var_labels = "Unadjusted comparison",
    .labels = c(lsmean = "Mean", lsmean_diff = "Difference in Means"),
    table_names = "unadjusted"
  ) %>%
  summarize_ancova(
    vars = "CHG",
    variables = list(arm = "ARMCD", covariates = c("BASE", "STRATA1")),
    conf_level = 0.95,
    var_labels = "Adjusted comparison (covariates BASE and STRATA1)",
    table_names = "adjusted"
  )

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

result

                                                     ARM A        ARM B           ARM C    
Parameter                                           (N=134)      (N=134)         (N=132)   
———————————————————————————————————————————————————————————————————————————————————————————
Unadjusted comparison                                                                      
  n                                                   68           73              62      
  Mean                                               3.68         5.07            3.09     
  Difference in Means                                             1.38            -0.59    
    95% CI                                                    (-2.76, 5.53)   (-4.91, 3.73)
    p-value                                                      0.5113          0.7873    
Adjusted comparison (covariates BASE and STRATA1)                                          
  n                                                   68           73              62      
  Adjusted Mean                                      4.06         3.57            3.34     
  Difference in Adjusted Means                                    -0.49           -0.72    
    95% CI                                                    (-3.28, 2.29)   (-3.57, 2.12)
    p-value                                                      0.7277          0.6165

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
adqs <- random.cdisc.data::cadqs

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

adqs_single <- adqs %>%
  filter(
    AVISIT == "WEEK 1 DAY 8",
    PARAMCD == "FKSI-FWB"
  ) %>%
  mutate(CHG = ifelse(BMEASIFL == "Y", CHG, NA))

Code

library(teal.modules.clinical)

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

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

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_ancova(
      label = "ANCOVA table",
      dataname = "ADQS",
      avisit = choices_selected(
        choices = value_choices(ADQS, "AVISIT"),
        selected = "WEEK 1 DAY 8"
      ),
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ACTARMCD")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      aval_var = choices_selected(
        choices = variable_choices(ADQS, c("CHG", "AVAL")),
        selected = "CHG"
      ),
      cov_var = choices_selected(
        choices = variable_choices(ADQS, c("BASE", "STRATA1", "SEX")),
        selected = "STRATA1"
      ),
      paramcd = choices_selected(
        choices = value_choices(ADQS, "PARAMCD", "PARAM"),
        selected = "FKSI-FWB"
      )
    )
  )
)
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://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
  ADQS <- random.cdisc.data::cadqs

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

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_ancova(
      label = "ANCOVA table",
      dataname = "ADQS",
      avisit = choices_selected(
        choices = value_choices(ADQS, "AVISIT"),
        selected = "WEEK 1 DAY 8"
      ),
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ACTARMCD")),
        selected = "ARM"
      ),
      arm_ref_comp = arm_ref_comp,
      aval_var = choices_selected(
        choices = variable_choices(ADQS, c("CHG", "AVAL")),
        selected = "CHG"
      ),
      cov_var = choices_selected(
        choices = variable_choices(ADQS, c("BASE", "STRATA1", "SEX")),
        selected = "STRATA1"
      ),
      paramcd = choices_selected(
        choices = value_choices(ADQS, "PARAMCD", "PARAM"),
        selected = "FKSI-FWB"
      )
    )
  )
)
shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:05:10 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    2024-09-12 [1] CRAN (R 4.4.1)
 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                  0.3.6    2024-10-25 [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                  0.4.0    2024-10-22 [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)
 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                      1.8.1    2024-10-28 [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   2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
 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   2024-09-20 [1] CRAN (R 4.4.1)
 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   2024-03-07 [1] CRAN (R 4.4.1)
 teal.code             * 0.5.0    2024-01-11 [1] CRAN (R 4.4.1)
 teal.data             * 0.6.0    2024-04-30 [1] CRAN (R 4.4.1)
 teal.logger             0.3.0    2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
 teal.modules.clinical * 0.9.1    2024-04-27 [1] CRAN (R 4.4.1)
 teal.reporter           0.3.1    2024-03-15 [1] CRAN (R 4.4.1)
 teal.slice            * 0.5.1    2024-04-30 [1] CRAN (R 4.4.1)
 teal.transform        * 0.5.0    2024-02-17 [1] CRAN (R 4.4.1)
 teal.widgets            0.4.2    2023-12-14 [1] CRAN (R 4.4.1)
 tern                  * 0.9.6    2024-09-24 [1] CRAN (R 4.4.1)
 tern.gee                0.1.5    2024-08-23 [1] CRAN (R 4.4.1)
 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                   3.0.2    2024-10-28 [1] CRAN (R 4.4.1)
 xfun                    0.49     2024-10-31 [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

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

`.lock` file

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

Download

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file