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.

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%)

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
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%)

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)

teal App

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
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
Code
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)

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
  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 18:10:16 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     2024-11-20
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

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