Filter and Sort an AE Specific Table

library(metalite.ae)

Overview

This vignette demonstrates how to generate a static AE-specific table reporting patients with drug-related adverse events by treatment group.

AE specific tables can contain many system organ classes and preferred terms. The filtering and sorting arguments of format_ae_specific() help focus the output on clinically relevant rows and present them in a useful order.

Define metadata

The example uses ADSL and ADAE data from the forestly package. The metadata follows the same approach used in the AE Specific Table vignette.

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_specific",
  population = "apat",
  observation = "wk12",
  parameter = "rel"
)

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 = "rel",
    term1 = "Drug-Related",
    term2 = "",
    subset = AEREL %in% c("POSSIBLE", "PROBABLE"),
    var = "AEDECOD",
    soc = "AEBODSYS",
    label = "Drug-related AEs"
  ) |>
  metalite::define_analysis(
    name = "ae_specific",
    title = "Participants with Drug-Related Adverse Events"
  ) |>
  metalite::meta_build()

Filter rows

Set filter_method to "percent" or "count", then use filter_criteria to define the minimum incidence required in at least one treatment group. Percentage criteria must be between 0 and 100; count criteria must be greater than 0.

The following example retains rows where at least one treatment group has an incidence of 6% or greater. To filter by participant count instead, set filter_method = "count" and pass the minimum count to filter_criteria.

rtf_dir <- if (dir.exists("vignettes/rtf")) "vignettes/rtf" else "rtf"

prepare_ae_specific(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  format_ae_specific(
    filter_method = "percent",
    filter_criteria = 6
  ) |>
  tlf_ae_specific(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_specific",
    meddra_version = "24.0",
    path_outtable = file.path(rtf_dir, "ae0specific4.rtf")
  )
#> The output is saved in/private/var/folders/yy/6bb11gtn52z0qwdg1_6kdbg00000gn/T/RtmpBCvLa1/Rbuild12c86435ade20/metalite.ae/vignettes/rtf/ae0specific4.rtf

Generated RTF file: ae0specific4.rtf

Sort rows

The sort_order argument accepts:

For count-based sorting, sort_column identifies the treatment group whose counts determine the order. Its value must match an entry in outdata$group.

The following example sorts rows by the Placebo participant count in descending order:

prepare_ae_specific(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  format_ae_specific(
    sort_order = "count_des",
    sort_column = "Placebo"
  ) |>
  tlf_ae_specific(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_specific",
    meddra_version = "24.0",
    path_outtable = file.path(rtf_dir, "ae0specific5.rtf")
  )
#> The output is saved in/private/var/folders/yy/6bb11gtn52z0qwdg1_6kdbg00000gn/T/RtmpBCvLa1/Rbuild12c86435ade20/metalite.ae/vignettes/rtf/ae0specific5.rtf

Generated RTF file: ae0specific5.rtf

Filter and sort rows

Filtering and sorting can be combined in one call. Filtering is applied first, and the retained rows are then sorted using the requested treatment group.

prepare_ae_specific(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  format_ae_specific(
    filter_method = "percent",
    filter_criteria = 6,
    sort_order = "count_des",
    sort_column = "Placebo"
  ) |>
  tlf_ae_specific(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_specific",
    meddra_version = "24.0",
    path_outtable = file.path(rtf_dir, "ae0specific6.rtf")
  )
#> The output is saved in/private/var/folders/yy/6bb11gtn52z0qwdg1_6kdbg00000gn/T/RtmpBCvLa1/Rbuild12c86435ade20/metalite.ae/vignettes/rtf/ae0specific6.rtf

Generated RTF file: ae0specific6.rtf