AET10

Most Common (\(\geq\) 5%) Adverse Events

Output

The “All Patients” column is not required in this table but for illustrative purposes we include it to show correct sorting.

Preview
Try this using WebR

Code

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(
    var = "ARM",
    split_fun = add_overall_level("All Patients", first = FALSE)
  ) %>%
  count_occurrences(vars = "AEDECOD")

tbl1 <- build_table(
  lyt = lyt,
  df = adae,
  alt_counts_df = adsl
)

tbl2 <- prune_table(
  tt = tbl1,
  prune_func = keep_rows(
    has_fraction_in_any_col(
      atleast = 0.05, # specify threshold
      col_names = levels(adsl$ARM)
    )
  )
)

result <- sort_at_path(
  tbl2,
  path = c("AEDECOD"),
  scorefun = score_occurrences
)

result

                A: Drug X    B: Placebo   C: Combination   All Patients
                 (N=134)      (N=134)        (N=132)         (N=400)   
———————————————————————————————————————————————————————————————————————
dcd D.2.1.5.3   47 (35.1%)   58 (43.3%)     57 (43.2%)     162 (40.5%) 
dcd A.1.1.1.1   50 (37.3%)   45 (33.6%)     63 (47.7%)     158 (39.5%) 
dcd B.2.2.3.1   48 (35.8%)   54 (40.3%)     51 (38.6%)     153 (38.2%) 
dcd A.1.1.1.2   48 (35.8%)   48 (35.8%)     50 (37.9%)     146 (36.5%) 
dcd B.2.1.2.1   49 (36.6%)   44 (32.8%)     52 (39.4%)     145 (36.2%) 
dcd D.1.1.1.1   50 (37.3%)   42 (31.3%)     51 (38.6%)     143 (35.8%) 
dcd D.1.1.4.2   48 (35.8%)   42 (31.3%)     50 (37.9%)     140 (35.0%) 
dcd B.1.1.1.1   47 (35.1%)   49 (36.6%)     43 (32.6%)     139 (34.8%) 
dcd C.2.1.2.1   35 (26.1%)   48 (35.8%)     55 (41.7%)     138 (34.5%) 
dcd C.1.1.1.3   43 (32.1%)   46 (34.3%)     43 (32.6%)     132 (33.0%)

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.

Preview
Try this using WebR

Code

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(
    var = "ARM",
    split_fun = add_overall_level("All Patients", first = FALSE)
  ) %>%
  count_occurrences(vars = "AEDECOD")

tbl1 <- build_table(
  lyt = lyt,
  df = adae,
  alt_counts_df = adsl
)

tbl2 <- prune_table(
  tt = tbl1,
  prune_func = keep_rows(
    has_fraction_in_any_col(
      atleast = 0.35, # specify threshold
      col_names = levels(adsl$ARM)
    )
  )
)

result <- sort_at_path(
  tbl2,
  path = c("AEDECOD"),
  scorefun = score_occurrences
)

result

                A: Drug X    B: Placebo   C: Combination   All Patients
                 (N=134)      (N=134)        (N=132)         (N=400)   
———————————————————————————————————————————————————————————————————————
dcd D.2.1.5.3   47 (35.1%)   58 (43.3%)     57 (43.2%)     162 (40.5%) 
dcd A.1.1.1.1   50 (37.3%)   45 (33.6%)     63 (47.7%)     158 (39.5%) 
dcd B.2.2.3.1   48 (35.8%)   54 (40.3%)     51 (38.6%)     153 (38.2%) 
dcd A.1.1.1.2   48 (35.8%)   48 (35.8%)     50 (37.9%)     146 (36.5%) 
dcd B.2.1.2.1   49 (36.6%)   44 (32.8%)     52 (39.4%)     145 (36.2%) 
dcd D.1.1.1.1   50 (37.3%)   42 (31.3%)     51 (38.6%)     143 (35.8%) 
dcd D.1.1.4.2   48 (35.8%)   42 (31.3%)     50 (37.9%)     140 (35.0%) 
dcd B.1.1.1.1   47 (35.1%)   49 (36.6%)     43 (32.6%)     139 (34.8%) 
dcd C.2.1.2.1   35 (26.1%)   48 (35.8%)     55 (41.7%)     138 (34.5%)

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.

Code

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adae <- random.cdisc.data::cadae

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

The desired frequency is specified in prune_freq. For illustration, frequency is set to \(\geq\) 35%.

Code

library(teal.modules.clinical)

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

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

## Reusable Configuration For Modules
ADAE <- data[["ADAE"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Adverse Event Table",
      dataname = "ADAE",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADAE, c("AETERM", "AEDECOD")),
        selected = c("AEDECOD")
      ),
      hlt = choices_selected(
        choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
        selected = NULL
      ),
      add_total = TRUE,
      event_type = "adverse event",
      prune_freq = 35,
    )
  )
)

shinyApp(app$ui, app$server)

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://insightsengineering.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
  ADAE <- random.cdisc.data::cadae

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

## Reusable Configuration For Modules
ADAE <- data[["ADAE"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Adverse Event Table",
      dataname = "ADAE",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADAE, c("AETERM", "AEDECOD")),
        selected = c("AEDECOD")
      ),
      hlt = choices_selected(
        choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
        selected = NULL
      ),
      add_total = TRUE,
      event_type = "adverse event",
      prune_freq = 35,
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-20 09:46:26 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)

─ Packages ───────────────────────────────────────────────────────────────────
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

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Output

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file