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.

R Code

Start Over

Run 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

_webr_editor_2 = Object {code: "lyt <- basic_table(show_colcounts = TRUE) %>%\n sp…DECOD\"),\n scorefun = score_occurrences\n)\n\nresult", options: Object, indicator: Ke}

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.

R Code

Start Over

Run 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

_webr_editor_3 = Object {code: "lyt <- basic_table(show_colcounts = TRUE) %>%\n sp…DECOD\"),\n scorefun = score_occurrences\n)\n\nresult", options: Object, indicator: Ke}

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

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
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.

Reproducibility

Timestamp

[1] "2025-04-05 17:48:52 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.2 (2024-10-31)
 os       Ubuntu 24.04.1 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-04-05
 pandoc   3.6.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.6.42 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version     date (UTC) lib source
 backports               1.5.0       2024-05-23 [1] RSPM
 brio                    1.1.5       2024-04-24 [1] RSPM
 broom                   1.0.8       2025-03-28 [1] RSPM
 bsicons                 0.1.2       2023-11-04 [1] RSPM
 bslib                   0.9.0       2025-01-30 [1] RSPM
 cachem                  1.1.0       2024-05-16 [1] RSPM
 callr                   3.7.6       2024-03-25 [1] RSPM
 checkmate               2.3.2       2024-07-29 [1] RSPM
 chromote                0.5.0       2025-03-21 [1] RSPM
 cli                     3.6.4       2025-02-13 [1] RSPM
 coda                    0.19-4.1    2024-01-31 [1] CRAN (R 4.4.2)
 codetools               0.2-20      2024-03-31 [2] CRAN (R 4.4.2)
 colorspace              2.1-1       2024-07-26 [1] RSPM
 curl                    6.2.2       2025-03-24 [1] RSPM
 digest                  0.6.37      2024-08-19 [1] RSPM
 dplyr                 * 1.1.4       2023-11-17 [1] RSPM
 emmeans                 1.11.0      2025-03-20 [1] RSPM
 estimability            1.5.1       2024-05-12 [1] RSPM
 evaluate                1.0.3       2025-01-10 [1] RSPM
 fastmap                 1.2.0       2024-05-15 [1] RSPM
 fontawesome             0.5.3       2024-11-16 [1] RSPM
 forcats                 1.0.0       2023-01-29 [1] RSPM
 formatR                 1.14        2023-01-17 [1] CRAN (R 4.4.2)
 formatters            * 0.5.10.9004 2025-04-05 [1] https://p~
 fs                      1.6.5       2024-10-30 [1] RSPM
 geepack                 1.3.12      2024-09-23 [1] RSPM
 generics                0.1.3       2022-07-05 [1] RSPM
 ggplot2                 3.5.1       2024-04-23 [1] RSPM
 glue                    1.8.0       2024-09-30 [1] RSPM
 gtable                  0.3.6       2024-10-25 [1] RSPM
 htmltools               0.5.8.1     2024-04-04 [1] RSPM
 htmlwidgets             1.6.4       2023-12-06 [1] RSPM
 httpuv                  1.6.15      2024-03-26 [1] RSPM
 jquerylib               0.1.4       2021-04-26 [1] RSPM
 jsonlite                2.0.0       2025-03-27 [1] RSPM
 knitr                   1.50        2025-03-16 [1] RSPM
 later                   1.4.1       2024-11-27 [1] RSPM
 lattice                 0.22-7      2025-04-02 [1] RSPM
 lifecycle               1.0.4       2023-11-07 [1] RSPM
 logger                  0.4.0       2024-10-22 [1] RSPM
 magrittr              * 2.0.3       2022-03-30 [1] RSPM
 MASS                    7.3-65      2025-02-28 [2] CRAN (R 4.4.2)
 Matrix                  1.7-3       2025-03-11 [1] CRAN (R 4.4.2)
 memoise                 2.0.1       2021-11-26 [1] RSPM
 mime                    0.13        2025-03-17 [1] RSPM
 multcomp                1.4-28      2025-01-29 [1] RSPM
 munsell                 0.5.1       2024-04-01 [1] RSPM
 mvtnorm                 1.3-3       2025-01-10 [1] RSPM
 nestcolor               0.1.3.9000  2025-04-05 [1] https://p~
 nlme                    3.1-168     2025-03-31 [1] RSPM
 pillar                  1.10.2      2025-04-05 [1] CRAN (R 4.4.2)
 pkgcache                2.2.3       2024-09-12 [1] RSPM
 pkgconfig               2.0.3       2019-09-22 [1] RSPM
 processx                3.8.6       2025-02-21 [1] RSPM
 promises                1.3.2       2024-11-28 [1] RSPM
 ps                      1.9.0       2025-02-18 [1] RSPM
 purrr                   1.0.4       2025-02-05 [1] RSPM
 R6                      2.6.1       2025-02-15 [1] RSPM
 random.cdisc.data       0.3.16.9002 2025-04-05 [1] https://p~
 rbibutils               2.3         2024-10-04 [1] RSPM
 Rcpp                    1.0.14      2025-01-12 [1] RSPM
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 rlang                   1.1.5       2025-01-17 [1] RSPM
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 scales                  1.3.0       2023-11-28 [1] RSPM
 sessioninfo             1.2.3       2025-02-05 [1] any (@1.2.3)
 shiny                 * 1.10.0      2024-12-14 [1] RSPM
 shinycssloaders         1.1.0       2024-07-30 [1] RSPM
 shinyjs                 2.1.0       2021-12-23 [1] RSPM
 shinyvalidate           0.1.3       2023-10-04 [1] RSPM
 shinyWidgets            0.9.0       2025-02-21 [1] RSPM
 stringi                 1.8.7       2025-03-27 [1] RSPM
 stringr                 1.5.1       2023-11-14 [1] RSPM
 survival                3.8-3       2024-12-17 [2] CRAN (R 4.4.2)
 teal                  * 0.16.0.9003 2025-04-05 [1] https://p~
 teal.code             * 0.6.1.9000  2025-04-05 [1] https://p~
 teal.data             * 0.7.0.9002  2025-04-05 [1] https://p~
 teal.logger             0.3.2.9000  2025-04-05 [1] https://p~
 teal.modules.clinical * 0.10.0.9002 2025-04-05 [1] https://p~
 teal.reporter           0.4.0.9004  2025-04-05 [1] https://p~
 teal.slice            * 0.6.0.9002  2025-04-05 [1] https://p~
 teal.transform        * 0.6.0.9001  2025-04-05 [1] https://p~
 teal.widgets            0.4.3.9001  2025-04-05 [1] https://p~
 tern                  * 0.9.7.9017  2025-04-05 [1] https://p~
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 testthat                3.2.3       2025-01-13 [1] RSPM
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 withr                   3.0.2       2024-10-28 [1] RSPM
 xfun                    0.52        2025-04-02 [1] RSPM
 xtable                  1.8-4       2019-04-21 [1] RSPM
 yaml                    2.3.10      2024-07-26 [1] RSPM
 zoo                     1.8-13      2025-02-22 [1] RSPM

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library
 [3] /github/home/R/x86_64-pc-linux-gnu-library/4.4
 * ── Packages attached to the search path.

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

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Download

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Output

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file