LBT07

Laboratory Test Results with Highest NCI CTCAE Grade Post-Baseline

Output

Code
lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by(
    "PARAM",
    label_pos = "topleft",
    split_label = obj_label(adlb_f$PARAM)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    required = "ATOXGR",
    .stats = "unique_count"
  ) %>%
  split_rows_by(
    "GRADE_DIR",
    label_pos = "topleft",
    split_label = obj_label(adlb_f$GRADE_DIR),
    split_fun = trim_levels_to_map(map)
  ) %>%
  count_abnormal_by_worst_grade(
    var = "GRADE_ANL",
    variables = list(id = "USUBJID", param = "PARAM", grade_dir = "GRADE_DIR"),
    .indent_mods = 4L
  ) %>%
  append_topleft("            Highest NCI CTCAE Grade")

result <- build_table(lyt = lyt, df = adlb_f, alt_counts_df = adsl)
result <- result %>% prune_table()

result
Parameter                                                                          
  Direction of Abnormality                 A: Drug X    B: Placebo   C: Combination
            Highest NCI CTCAE Grade         (N=134)      (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————
Alanine Aminotransferase Measurement (n)      134          134            132      
  LOW                                                                              
            1                              15 (11.2%)   11 (8.2%)      15 (11.4%)  
            2                              18 (13.4%)   24 (17.9%)     17 (12.9%)  
            3                              16 (11.9%)   24 (17.9%)     16 (12.1%)  
            4                              17 (12.7%)   10 (7.5%)      18 (13.6%)  
            Any                            66 (49.3%)   69 (51.5%)      66 (50%)   
C-Reactive Protein Measurement (n)            134          134            132      
  LOW                                                                              
            1                              21 (15.7%)   22 (16.4%)     12 (9.1%)   
            2                              24 (17.9%)   22 (16.4%)     18 (13.6%)  
            3                              29 (21.6%)   21 (15.7%)     25 (18.9%)  
            4                              10 (7.5%)    16 (11.9%)     22 (16.7%)  
            Any                            84 (62.7%)   81 (60.4%)     77 (58.3%)  
  HIGH                                                                             
            1                              20 (14.9%)   22 (16.4%)     22 (16.7%)  
            2                              21 (15.7%)   15 (11.2%)     14 (10.6%)  
            3                              25 (18.7%)   16 (11.9%)     20 (15.2%)  
            4                              13 (9.7%)    20 (14.9%)     11 (8.3%)   
            Any                             79 (59%)    73 (54.5%)     67 (50.8%)  
Immunoglobulin A Measurement (n)              134          134            132      
  HIGH                                                                             
            1                              19 (14.2%)    12 (9%)       18 (13.6%)  
            2                              17 (12.7%)   24 (17.9%)     19 (14.4%)  
            3                              21 (15.7%)   23 (17.2%)     23 (17.4%)  
            4                              13 (9.7%)    13 (9.7%)       9 (6.8%)   
            Any                            70 (52.2%)   72 (53.7%)     69 (52.3%)

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Code
library(tern)
library(dplyr)
library(forcats)

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

adlb_labels <- var_labels(adlb)

# 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)

# Select worst post-baseline records.
adlb_f <- adlb %>%
  filter(ATOXGR != "<Missing>") %>%
  filter(ONTRTFL == "Y") %>%
  filter(WGRLOFL == "Y" | WGRHIFL == "Y")

var_labels(adlb_f) <- adlb_labels

# Derive GRADE_DIR and GRADE_ANL to use in layout from ATOXGR
adlb_f <- adlb_f %>%
  mutate(
    GRADE_DIR = factor(
      case_when(
        ATOXGR %in% c("-1", "-2", "-3", "-4") & .data$WGRLOFL == "Y" ~ "LOW",
        ATOXGR == "0" ~ "ZERO",
        ATOXGR %in% c("1", "2", "3", "4") & .data$WGRHIFL == "Y" ~ "HIGH",
        TRUE ~ "NONE"
      ),
      levels = c("LOW", "ZERO", "HIGH", "NONE")
    ),
    GRADE_ANL = forcats::fct_relevel(
      forcats::fct_recode(ATOXGR,
        `1` = "-1", `2` = "-2", `3` = "-3", `4` = "-4"
      ),
      c("0", "1", "2", "3", "4")
    )
  ) %>%
  var_relabel(
    GRADE_DIR = "Direction of Abnormality",
    GRADE_ANL = "Analysis Grade"
  )

# Construct analysis map
map <- expand.grid(
  PARAM = levels(adlb$PARAM),
  GRADE_DIR = c("LOW", "HIGH"),
  GRADE_ANL = as.character(1:4),
  stringsAsFactors = FALSE
) %>%
  arrange(PARAM, desc(GRADE_DIR), GRADE_ANL)

teal App

Code
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADLB <- random.cdisc.data::cadlb %>%
    filter(!AVISIT %in% c("SCREENING", "BASELINE"))
})
datanames <- c("ADSL", "ADLB")
datanames(data) <- datanames
Warning: `datanames<-()` was deprecated in teal.data 0.7.0.
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"]]
ADLB <- data[["ADLB"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_abnormality_by_worst_grade(
      label = "Laboratory Test Results with Highest Grade Post-Baseline",
      dataname = "ADLB",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      paramcd = choices_selected(
        choices = value_choices(ADLB, "PARAMCD", "PARAM"),
        selected = c("ALT", "CRP", "IGA")
      ),
      add_total = FALSE
    )
  ),
  filter = (
    teal_slices(
      teal_slice("ADSL", "SAFFL", selected = "Y"),
      teal_slice("ADLB", "ONTRTFL", selected = "Y")
    )
  )
)

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.

#| '!! 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, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADLB <- random.cdisc.data::cadlb %>%
    filter(!AVISIT %in% c("SCREENING", "BASELINE"))
})
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_abnormality_by_worst_grade(
      label = "Laboratory Test Results with Highest Grade Post-Baseline",
      dataname = "ADLB",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      paramcd = choices_selected(
        choices = value_choices(ADLB, "PARAMCD", "PARAM"),
        selected = c("ALT", "CRP", "IGA")
      ),
      add_total = FALSE
    )
  ),
  filter = (
    teal_slices(
      teal_slice("ADSL", "SAFFL", selected = "Y"),
      teal_slice("ADLB", "ONTRTFL", selected = "Y")
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2025-06-11 17:48:14 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11)
 os       Ubuntu 24.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-06-11
 pandoc   3.6.4 @ /usr/bin/ (via rmarkdown)
 quarto   1.7.31 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version   date (UTC) lib source
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 survival                3.8-3     2024-12-17 [2] CRAN (R 4.5.0)
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 teal.code             * 0.6.1     2025-02-14 [1] RSPM
 teal.data             * 0.7.0     2025-01-28 [1] RSPM
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 [2] /usr/local/lib/R/library
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 * ── Packages attached to the search path.

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

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