LBT01

Laboratory Test Results and Change from Baseline by Visit

Code
# Define the split function
split_fun <- drop_split_levels

afun <- function(x, .var, .spl_context, ...) {
  n_fun <- sum(!is.na(x), na.rm = TRUE)
  if (n_fun == 0) {
    mean_sd_fun <- c(NA, NA)
    median_fun <- NA
    min_max_fun <- c(NA, NA)
  } else {
    mean_sd_fun <- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))
    median_fun <- median(x, na.rm = TRUE)
    min_max_fun <- c(min(x), max(x))
  }
  is_chg <- .var == "CHG"
  is_baseline <- .spl_context$value[which(.spl_context$split == "AVISIT")] == "BASELINE"
  if (is_baseline && is_chg) n_fun <- mean_sd_fun <- median_fun <- min_max_fun <- NULL

  in_rows(
    "n" = n_fun,
    "Mean (SD)" = mean_sd_fun,
    "Median" = median_fun,
    "Min - Max" = min_max_fun,
    .formats = list("n" = "xx", "Mean (SD)" = "xx.xx (xx.xx)", "Median" = "xx.xx", "Min - Max" = "xx.xx - xx.xx"),
    .format_na_strs = list("n" = "NE", "Mean (SD)" = "NE (NE)", "Median" = "NE", "Min - Max" = "NE - NE")
  )
}

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("PARAM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adlb_f$PARAM)) %>%
  split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adlb_f$AVISIT)) %>%
  split_cols_by_multivar(
    vars = c("AVAL", "CHG"),
    varlabels = c("Value at Visit", "Change from\nBaseline")
  ) %>%
  analyze_colvars(afun = afun)

result <- build_table(lyt, adlb_f)
result
                                                  A: Drug X                        B: Placebo                      C: Combination         
                                                         Change from                       Change from                       Change from  
Parameter                              Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   
  Analysis Visit                          (N=804)          (N=804)          (N=670)          (N=670)          (N=792)          (N=792)    
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Alanine Aminotransferase Measurement                                                                                                      
  BASELINE                                                                                                                                
    n                                       134                               134                               132                       
    Mean (SD)                           17.74 (9.93)                      18.71 (9.83)                      19.46 (9.08)                  
    Median                                 17.46                             18.19                             18.97                      
    Min - Max                           0.00 - 44.06                      1.48 - 54.40                      0.57 - 39.81                  
  WEEK 1 DAY 8                                                                                                                            
    n                                       134              134               0                0               132              132      
    Mean (SD)                           16.75 (9.08)    -0.99 (13.49)       NE (NE)          NE (NE)        19.61 (9.27)     0.14 (12.85) 
    Median                                 16.02            -1.28              NE               NE             19.00             0.06     
    Min - Max                           0.05 - 36.30    -31.31 - 27.89      NE - NE          NE - NE        0.91 - 44.75    -32.45 - 38.85
  WEEK 2 DAY 15                                                                                                                           
    n                                       134              134              134              134              132              132      
    Mean (SD)                           17.82 (9.60)     0.08 (14.15)     18.82 (9.73)     0.11 (14.45)     16.55 (8.15)    -2.92 (12.64) 
    Median                                 15.92             0.28            17.96            -0.93            17.02            -1.11     
    Min - Max                           0.40 - 44.33    -32.89 - 40.55    0.18 - 44.34    -45.93 - 29.85    0.35 - 34.69    -28.36 - 23.98
  WEEK 3 DAY 22                                                                                                                           
    n                                       134              134              134              134              132              132      
    Mean (SD)                           18.37 (9.30)     0.63 (13.85)     17.65 (9.58)    -1.06 (13.58)     16.75 (9.54)    -2.71 (13.22) 
    Median                                 18.11             1.13            17.68            -0.49            15.10            -2.66     
    Min - Max                           0.59 - 41.73    -40.09 - 31.24    0.02 - 38.61    -46.30 - 31.38    0.48 - 39.23    -30.63 - 26.51
  WEEK 4 DAY 29                                                                                                                           
    n                                       134              134              134              134              132              132      
    Mean (SD)                          19.17 (10.95)     1.44 (15.39)    17.22 (10.64)    -1.48 (15.20)     17.92 (9.32)    -1.54 (12.63) 
    Median                                 17.41             0.88            15.88            -2.96            17.71            -1.53     
    Min - Max                           0.93 - 54.24    -32.93 - 46.98    0.39 - 47.96    -41.45 - 43.08    0.25 - 41.27    -30.33 - 27.99
  WEEK 5 DAY 36                                                                                                                           
    n                                       134              134              134              134              132              132      
    Mean (SD)                           19.22 (9.47)     1.48 (14.49)     18.01 (9.92)    -0.69 (14.65)     18.51 (9.43)    -0.95 (12.92) 
    Median                                 19.80             2.31            18.28             1.82            19.26            -2.48     
    Min - Max                           0.01 - 43.42    -40.08 - 30.07    0.11 - 40.64    -47.60 - 26.04    0.02 - 37.46    -29.78 - 25.00

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.

In order to generate the LBT01 standard tabulation, the adlb dataset may be pre-processed so as to discriminate baseline from follow-up visits.

Code
library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adlb <- random.cdisc.data::cadlb

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adlb <- df_explicit_na(adlb) %>%
  filter(ANL01FL == "Y")

# For illustration purpose, the example focuses on "Alanine Aminotransferase
# Measurement" starting from baseline, while excluding visit at week 1 for
# subjects who were prescribed a placebo.
adlb_f <- adlb %>%
  dplyr::filter(
    PARAM == "Alanine Aminotransferase Measurement" &
      !(ACTARM == "B: Placebo" & AVISIT == "WEEK 1 DAY 8") &
      AVISIT != "SCREENING"
  )

teal App

Code
library(teal.modules.clinical)

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

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

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Laboratory Test Results and Change from Baseline by Visit",
      dataname = "ADLB",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADLB, c("PARAM", "AVISIT")),
        selected = c("AVISIT")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADLB, c("AVAL", "CHG")),
        selected = c("AVAL", "CHG")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADLB, "PARAMCD", "PARAM"),
        selected = "ALT"
      ),
      parallel_vars = TRUE
    )
  ),
  filter = teal_slices(teal_slice("ADLB", "AVAL", selected = NULL))
)

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
  ADLB <- random.cdisc.data::cadlb

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

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADLB <- data[["ADLB"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Laboratory Test Results and Change from Baseline by Visit",
      dataname = "ADLB",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADLB, c("PARAM", "AVISIT")),
        selected = c("AVISIT")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADLB, c("AVAL", "CHG")),
        selected = c("AVAL", "CHG")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADLB, "PARAMCD", "PARAM"),
        selected = "ALT"
      ),
      parallel_vars = TRUE
    )
  ),
  filter = teal_slices(teal_slice("ADLB", "AVAL", selected = NULL))
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 17:55:27 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 ───────────────────────────────────────────────────────────────────
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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