AOVT01

ANCOVA for Multiple End Points

Code
# Define the split function
split_fun <- drop_split_levels

l <- basic_table() %>%
  split_cols_by("ARMCD", ref_group = "ARM A") %>%
  split_rows_by("PARAMCD",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adqs_multi$PARAMCD)
  ) %>%
  summarize_ancova(
    vars = "CHG",
    variables = list(
      arm = "ARMCD",
      covariates = c("BASE", "STRATA1")
    ),
    conf_level = 0.95,
    var_labels = "Adjusted mean"
  )

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

result
Parameter Code                     ARM A       ARM B           ARM C    
————————————————————————————————————————————————————————————————————————
BFIALL                                                                  
  Adjusted mean                                                         
    n                               134         134             132     
    Adjusted Mean                  4.47        6.33            4.02     
    Difference in Adjusted Means               1.85            -0.46    
      95% CI                               (-0.14, 3.85)   (-2.45, 1.54)
      p-value                                 0.0679          0.6539    
FATIGI                                                                  
  Adjusted mean                                                         
    n                               134         134             132     
    Adjusted Mean                  5.42        4.83            4.56     
    Difference in Adjusted Means               -0.59           -0.86    
      95% CI                               (-2.58, 1.41)   (-2.87, 1.15)
      p-value                                 0.5644          0.4026    
FKSI-FWB                                                                
  Adjusted mean                                                         
    n                               134         134             132     
    Adjusted Mean                  4.29        3.51            3.06     
    Difference in Adjusted Means               -0.79           -1.24    
      95% CI                               (-2.71, 1.14)   (-3.17, 0.69)
      p-value                                 0.4221          0.2088    
FKSI-TSE                                                                
  Adjusted mean                                                         
    n                               134         134             132     
    Adjusted Mean                  4.70        3.84            4.45     
    Difference in Adjusted Means               -0.86           -0.25    
      95% CI                               (-2.80, 1.09)   (-2.20, 1.70)
      p-value                                 0.3858          0.8007    
FKSIALL                                                                 
  Adjusted mean                                                         
    n                               134         134             132     
    Adjusted Mean                  5.03        5.82            6.44     
    Difference in Adjusted Means               0.79            1.42     
      95% CI                               (-1.17, 2.76)   (-0.56, 3.39)
      p-value                                 0.4288          0.1591

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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_multi <- filter(adqs, AVISIT == "WEEK 1 DAY 8")

teal 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 = c("FKSI-FWB", "BFIALL")
      )
    )
  )
)

shinyApp(app$ui, app$server)

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://pharmaverse.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 = c("FKSI-FWB", "BFIALL")
      )
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 17:48:03 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)

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 [2] /usr/local/lib/R/library

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