AOVT02

ANCOVA with Single End Point and Customized Table

Output

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.

#| '!! 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
  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] "2024-11-21 15:04:59 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     2024-11-21
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
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 R6                      2.5.1    2021-08-19 [1] RSPM
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 teal                  * 0.15.2   2024-03-07 [1] RSPM
 teal.code             * 0.5.0    2024-01-11 [1] RSPM
 teal.data             * 0.6.0    2024-04-30 [1] RSPM
 teal.logger             0.3.0    2024-10-24 [1] RSPM
 teal.modules.clinical * 0.9.1    2024-04-27 [1] RSPM
 teal.reporter           0.3.1    2024-03-15 [1] RSPM
 teal.slice            * 0.5.1    2024-04-30 [1] RSPM
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

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Output

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file