The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.

Generate a Static AE Summary Table in GT format

library(metalite.ae)

Overview

This vignette demonstrates how to generate a static AE summary table in gt format reporting - The number and percentage of participants with any AEs by treatment group; - The number and percentage of participants with drug-related AEs by treatment group; - The number and percentage of participants with serious AEs by treatment group.

The workflow uses three functions from metalite.ae:

An optional extension adds risk-difference inference:

Generate an AE summary table

The example uses ADSL and ADAE data from the forestly package.

Step 1: Define metadata

# Define metadata
adsl <- forestly::forestly_adsl
adae <- forestly::forestly_adae

adsl$TRT01A <- factor(
  adsl$TRT01A,
  levels = c("Xanomeline Low Dose", "Placebo"),
  labels = c("Low Dose", "Placebo")
)
adae$TRTA <- factor(
  adae$TRTA,
  levels = c("Xanomeline Low Dose", "Placebo"),
  labels = c("Low Dose", "Placebo")
)

analysis_plan <- metalite::plan(
  analysis = "ae_summary",
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)

meta <- metalite::meta_adam(observation = adae, population = adsl) |>
  metalite::define_plan(analysis_plan) |>
  metalite::define_population(
    name = "apat",
    var = c(
      "USUBJID", "SAFFL", "TRT01A", "TRTDUR",
      "SITEID", "SEX", "RACE", "AGE"
    ),
    group = "TRT01A",
    subset = SAFFL == "Y",
    label = "All Participants as Treated"
  ) |>
  metalite::define_observation(
    name = "wk12",
    var = c(
      "USUBJID", "SAFFL", "TRTA", "AEDECOD", "AEBODSYS", "AEREL",
      "AESER", "AEOUT", "AEACN", "AESDTH", "ASTDT", "AENDT"
    ),
    group = "TRTA",
    subset = SAFFL == "Y",
    label = "Weeks 0 to 12"
  ) |>
  metalite::define_parameter(
    name = "any",
    term1 = "",
    term2 = "",
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "All AEs"
  ) |>
  metalite::define_parameter(
    name = "rel",
    term1 = "Drug-Related",
    term2 = "",
    subset = AEREL %in% c("POSSIBLE", "PROBABLE"),
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "Drug-related AEs"
  ) |>
  metalite::define_parameter(
    name = "ser",
    term1 = "Serious",
    term2 = "",
    subset = AESER == "Y",
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "Serious AEs"
  ) |>
  metalite::define_analysis(
    name = "ae_summary",
    title = "Adverse Event Summary"
  ) |>
  metalite::meta_build()
Click to show the output 
meta
#> ADaM metadata: 
#>    .$data_population     Population data with 170 subjects 
#>    .$data_observation    Observation data with 736 records 
#>    .$plan    Analysis plan with 1 plans 
#> 
#> 
#>   Analysis population type:
#>     name        id    group
#> 1 'apat' 'USUBJID' 'TRT01A'
#>                                                      var       subset
#> 1 USUBJID, SAFFL, TRT01A, TRTDUR, SITEID, SEX, RACE, AGE SAFFL == 'Y'
#>                           label
#> 1 'All Participants as Treated'
#> 
#> 
#>   Analysis observation type:
#>     name        id  group
#> 1 'wk12' 'USUBJID' 'TRTA'
#>                                                                                         var
#> 1 USUBJID, SAFFL, TRTA, AEDECOD, AEBODSYS, AEREL, AESER, AEOUT, AEACN, AESDTH, ASTDT, AENDT
#>         subset           label
#> 1 SAFFL == 'Y' 'Weeks 0 to 12'
#> 
#> 
#>   Analysis parameter type:
#>    name              label                               subset
#> 1 'any'          'All AEs'                                     
#> 2 'rel' 'Drug-related AEs' AEREL %in% c('POSSIBLE', 'PROBABLE')
#> 3 'ser'      'Serious AEs'                         AESER == 'Y'
#> 
#> 
#>   Analysis function:
#>           name                          label
#> 1 'ae_summary' 'Table: adverse event summary'

Step 2: Generate the GT table

prepare_ae_summary() uses the definitions in meta to calculate the summary results. format_ae_summary() formats those results, and gt_ae_summary() creates the GT table.

prepare_ae_summary(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
) |>
  format_ae_summary() |>
  gt_ae_summary(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_summary"
  )
#> [1] "Adverse Event Summary"       "Weeks 0 to 12"              
#> [3] "All Participants as Treated"
Adverse Event Summary
Weeks 0 to 12
All Participants as Treated
Low Dose
Placebo
Total
n (%) n (%) n (%)
Participants in population 84
86
170
with one or more adverse events 77 (91.7) 69 (80.2) 146 (85.9)
with no adverse events 7 (8.3) 17 (19.8) 24 (14.1)
with drug-relateda adverse events 73 (86.9) 44 (51.2) 117 (68.8)
with serious adverse events 1 (1.2) 0 (0.0) 1 (0.6)
aDetermined by the investigator to be related to the drug.
Source: [CDISCpilot: adam-adsl; adae]

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.