EGT02

ECG Abnormalities (EGT02_1 & EGT02_2)

---

Output

ECG Abnormalities Regardless of Abnormality at Baseline

Preview

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "PARAM",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adeg_f$PARAM)
  ) %>%
  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = FALSE) %>%
  append_varlabels(adeg_f, "ANRIND", indent = 1L)

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

Assessment        A: Drug X        B: Placebo     C: Combination
  Abnormality      (N=134)          (N=134)          (N=132)    
————————————————————————————————————————————————————————————————
Heart Rate                                                      
  Low           40/134 (29.9%)   43/134 (32.1%)    37/132 (28%) 
  High          39/134 (29.1%)   45/134 (33.6%)   36/132 (27.3%)
QT Duration                                                     
  Low           33/134 (24.6%)   44/134 (32.8%)   47/132 (35.6%)
  High          30/134 (22.4%)   42/134 (31.3%)   34/132 (25.8%)
RR Duration                                                     
  Low           45/134 (33.6%)   26/134 (19.4%)   38/132 (28.8%)
  High          29/134 (21.6%)   49/134 (36.6%)   27/132 (20.5%)

Try this using WebR

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.

ECG Abnormalities Among Subjects Without Abnormality at Baseline

Preview

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  split_rows_by(
    "PARAM",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adeg_f$PARAM)
  ) %>%
  count_abnormal("ANRIND", abnormal = list(Low = "LOW", High = "HIGH"), exclude_base_abn = TRUE) %>%
  append_varlabels(adeg_f, "ANRIND", indent = 1L)

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

Assessment        A: Drug X        B: Placebo     C: Combination
  Abnormality      (N=134)          (N=134)          (N=132)    
————————————————————————————————————————————————————————————————
Heart Rate                                                      
  Low           39/128 (30.5%)   42/124 (33.9%)   33/117 (28.2%)
  High          37/127 (29.1%)   43/129 (33.3%)   31/122 (25.4%)
QT Duration                                                     
  Low           29/113 (25.7%)   42/126 (33.3%)   43/124 (34.7%)
  High           30/125 (24%)    40/130 (30.8%)   30/119 (25.2%)
RR Duration                                                     
  Low           45/129 (34.9%)   26/123 (21.1%)   37/126 (29.4%)
  High          27/127 (21.3%)   43/122 (35.2%)    25/125 (20%) 

Try this using WebR

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.

Data Setup

library(dplyr)
library(tern)

adsl <- random.cdisc.data::cadsl
adeg <- random.cdisc.data::cadeg

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

# Note:  We keep only post-baseline for analysis.
adeg_f <- adeg %>%
  filter(ONTRTFL == "Y") %>%
  filter(PARAM %in% c("Heart Rate", "QT Duration", "RR Duration")) %>%
  filter(ANRIND != "<Missing>") %>%
  var_relabel(
    PARAM = "Assessment",
    ANRIND = "Abnormality"
  )

teal App

Preview

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

Warning: `datanames<-()` was deprecated in teal.data 0.6.1.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data

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_abnormality(
      label = "Abnormality Table",
      dataname = "ADEG",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEG, subset = c("EGCAT", "PARAM", "AVISIT")),
        selected = c("PARAM"),
        keep_order = TRUE
      ),
      grade = choices_selected(
        choices = variable_choices(ADEG, subset = "ANRIND"),
        selected = "ANRIND",
        fixed = TRUE
      ),
      abnormal = list(Low = "LOW", High = "HIGH"),
      exclude_base_abn = FALSE
    )
  )
)

shinyApp(app$ui, app$server)

Try this using shinylive

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://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_abnormality(
      label = "Abnormality Table",
      dataname = "ADEG",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEG, subset = c("EGCAT", "PARAM", "AVISIT")),
        selected = c("PARAM"),
        keep_order = TRUE
      ),
      grade = choices_selected(
        choices = variable_choices(ADEG, subset = "ANRIND"),
        selected = "ANRIND",
        fixed = TRUE
      ),
      abnormal = list(Low = "LOW", High = "HIGH"),
      exclude_base_abn = FALSE
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-20 18:13:46 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     2024-11-20
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

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 teal.logger             0.3.0.9002  2024-11-20 [1] https://p~
 teal.modules.clinical * 0.9.1.9031  2024-11-20 [1] https://p~
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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