EGT03

Shift Table of ECG Interval Data – Baseline Versus Minimum/Maximum Post-Baseline

For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N. For illustration purposes, missing data are added to the example dataset.

Code
set.seed(123, kind = "Mersenne-Twister")

adeg_f_pbmin$BNRIND <- factor(
  adeg_f_pbmin$BNRIND,
  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
  labels = c("LOW", "NORMAL", "HIGH", "Missing")
)
adeg_f_pbmin$ANRIND <- factor(
  adeg_f_pbmin$ANRIND,
  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
  labels = c("LOW", "NORMAL", "HIGH", "Missing")
)

adeg_f_pbmin$BNRIND[sample(seq_len(nrow(adeg_f_pbmin)), size = 5)] <- "Missing"
adeg_f_pbmin$ANRIND[sample(seq_len(nrow(adeg_f_pbmin)), size = 5)] <- "Missing"

attr(adeg_f_pbmin$ANRIND, "label") <- "Analysis Reference Range Indicator"
attr(adeg_f_pbmin$BNRIND, "label") <- "Baseline Reference Range Indicator"

# Temporary solution for overarching column
adeg_f_pbmin <- adeg_f_pbmin %>% mutate(min_label = "Minimum Post-Baseline Assessment")

# Define the split function
split_fun <- drop_split_levels

lyt <- basic_table() %>%
  split_cols_by("min_label") %>%
  split_cols_by("ANRIND") %>%
  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmin$ARMCD)) %>%
  add_rowcounts() %>%
  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
  append_varlabels(adeg_f_pbmin, c("BNRIND"), indent = 1L)

result <- build_table(
  lyt = lyt,
  df = adeg_f_pbmin
)

result
Planned Arm Code                           Minimum Post-Baseline Assessment     
  Baseline Reference Range Indicator      LOW         NORMAL     HIGH   Missing 
————————————————————————————————————————————————————————————————————————————————
ARM A (N=134)                                                                   
  LOW                                   1 (0.7%)     5 (3.7%)     0        0    
  NORMAL                               36 (26.9%)   83 (61.9%)    0     1 (0.7%)
  HIGH                                  2 (1.5%)      4 (3%)      0        0    
  Missing                               1 (0.7%)        0         0     1 (0.7%)
ARM B (N=134)                                                                   
  LOW                                   1 (0.7%)     9 (6.7%)     0        0    
  NORMAL                               40 (29.9%)    75 (56%)     0     2 (1.5%)
  HIGH                                  1 (0.7%)      4 (3%)      0        0    
  Missing                               1 (0.7%)     1 (0.7%)     0        0    
ARM C (N=132)                                                                   
  LOW                                    4 (3%)     11 (8.3%)     0        0    
  NORMAL                               31 (23.5%)   75 (56.8%)    0        0    
  HIGH                                  1 (0.8%)     8 (6.1%)     0     1 (0.8%)
  Missing                               1 (0.8%)        0         0        0

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.

For the EGT03 template, data imputation should be avoided, and missing data explicit and accounted for, so the contingency table sum adds up to the group N. For illustration purpose, missing data are added to the example dataset.

Code
set.seed(123, kind = "Mersenne-Twister")

adeg_f_pbmax$BNRIND <- factor(
  adeg_f_pbmax$BNRIND,
  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
  labels = c("LOW", "NORMAL", "HIGH", "Missing")
)
adeg_f_pbmax$ANRIND <- factor(
  adeg_f_pbmax$ANRIND,
  levels = c("LOW", "NORMAL", "HIGH", "Missing"),
  labels = c("LOW", "NORMAL", "HIGH", "Missing")
)

adeg_f_pbmax$BNRIND[sample(seq_len(nrow(adeg_f_pbmax)), size = 5)] <- "Missing"
adeg_f_pbmax$ANRIND[sample(seq_len(nrow(adeg_f_pbmax)), size = 5)] <- "Missing"

attr(adeg_f_pbmax$ANRIND, "label") <- "Analysis Reference Range Indicator"
attr(adeg_f_pbmax$BNRIND, "label") <- "Baseline Reference Range Indicator"

# Temporary solution for overarching column
adeg_f_pbmax <- adeg_f_pbmax %>% mutate(max_label = "Maximum Post-Baseline Assessment")

# Define the split function
split_fun <- drop_split_levels

lyt <- basic_table() %>%
  split_cols_by("max_label") %>%
  split_cols_by("ANRIND") %>%
  split_rows_by("ARMCD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adeg_f_pbmax$ARMCD)) %>%
  add_rowcounts() %>%
  analyze_vars("BNRIND", denom = "N_row", .stats = "count_fraction") %>%
  append_varlabels(adeg_f_pbmax, c("BNRIND"), indent = 1L)

result <- build_table(
  lyt = lyt,
  df = adeg_f_pbmax
)

result
Planned Arm Code                           Maximum Post-Baseline Assessment     
  Baseline Reference Range Indicator   LOW      NORMAL        HIGH      Missing 
————————————————————————————————————————————————————————————————————————————————
ARM A (N=134)                                                                   
  LOW                                   0      2 (1.5%)      4 (3%)        0    
  NORMAL                                0     88 (65.7%)   31 (23.1%)   1 (0.7%)
  HIGH                                  0      5 (3.7%)     1 (0.7%)       0    
  Missing                               0         0         1 (0.7%)    1 (0.7%)
ARM B (N=134)                                                                   
  LOW                                   0       8 (6%)      2 (1.5%)       0    
  NORMAL                                0     76 (56.7%)   39 (29.1%)   2 (1.5%)
  HIGH                                  0      3 (2.2%)     2 (1.5%)       0    
  Missing                               0      1 (0.7%)     1 (0.7%)       0    
ARM C (N=132)                                                                   
  LOW                                   0     12 (9.1%)     3 (2.3%)       0    
  NORMAL                                0     79 (59.8%)   27 (20.5%)      0    
  HIGH                                  0      5 (3.8%)      4 (3%)     1 (0.8%)
  Missing                               0         0         1 (0.8%)       0

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)

adeg <- random.cdisc.data::cadeg

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

adeg_labels <- var_labels(adeg)

adeg_f_pbmin <- subset(
  adeg,
  PARAMCD == "HR" & # Heart Rate
    SAFFL == "Y" & # "Safety Population Flag"
    ONTRTFL == "Y" & # "On Treatment Record Flag"
    AVISIT == "POST-BASELINE MINIMUM" # "Analysis Visit"
)

adeg_f_pbmax <- subset(
  adeg,
  PARAMCD == "HR" & # Heart Rate
    SAFFL == "Y" & # "Safety Population Flag"
    ONTRTFL == "Y" & # "On Treatment Record Flag"
    AVISIT == "POST-BASELINE MAXIMUM" # "Analysis Visit"
)

var_labels(adeg_f_pbmin) <- adeg_labels
var_labels(adeg_f_pbmax) <- adeg_labels

teal App

Code
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADEG <- random.cdisc.data::cadeg
})
datanames <- c("ADSL", "ADEG")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_shift_by_arm(
      label = "Shift by Arm Table",
      dataname = "ADEG",
      arm_var = choices_selected(
        variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      paramcd = choices_selected(
        value_choices(ADEG, "PARAMCD"),
        selected = "HR"
      ),
      visit_var = choices_selected(
        value_choices(ADEG, "AVISIT"),
        selected = "POST-BASELINE MINIMUM"
      ),
      aval_var = choices_selected(
        variable_choices(ADEG, subset = "ANRIND"),
        selected = "ANRIND", fixed = TRUE
      ),
      baseline_var = choices_selected(
        variable_choices(ADEG, subset = "BNRIND"),
        selected = "BNRIND", fixed = TRUE
      ),
      treatment_flag_var = choices_selected(
        variable_choices(ADEG, subset = "ONTRTFL"),
        selected = "ONTRTFL", fixed = TRUE
      ),
      treatment_flag = choices_selected(
        value_choices(ADEG, "ONTRTFL"),
        selected = "Y", fixed = TRUE
      )
    )
  ),
  filter = teal_slices(teal_slice("ADSL", "SAFFL", 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.

#| 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
  ADEG <- random.cdisc.data::cadeg
})
datanames <- c("ADSL", "ADEG")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_shift_by_arm(
      label = "Shift by Arm Table",
      dataname = "ADEG",
      arm_var = choices_selected(
        variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      paramcd = choices_selected(
        value_choices(ADEG, "PARAMCD"),
        selected = "HR"
      ),
      visit_var = choices_selected(
        value_choices(ADEG, "AVISIT"),
        selected = "POST-BASELINE MINIMUM"
      ),
      aval_var = choices_selected(
        variable_choices(ADEG, subset = "ANRIND"),
        selected = "ANRIND", fixed = TRUE
      ),
      baseline_var = choices_selected(
        variable_choices(ADEG, subset = "BNRIND"),
        selected = "BNRIND", fixed = TRUE
      ),
      treatment_flag_var = choices_selected(
        variable_choices(ADEG, subset = "ONTRTFL"),
        selected = "ONTRTFL", fixed = TRUE
      ),
      treatment_flag = choices_selected(
        value_choices(ADEG, "ONTRTFL"),
        selected = "Y", fixed = TRUE
      )
    )
  ),
  filter = teal_slices(teal_slice("ADSL", "SAFFL", selected = "Y"))
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:22:16 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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