Customize Columns in 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.

The display argument of format_ae_specific() controls which statistics appear in an AE specific table. This vignette demonstrates how to add risk difference inference, adverse event duration, and event frequency statistics.

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()

Select columns

Use display to select statistics and set their order. Available options are:

• "n": number of participants with an adverse event.
• "prop": proportion of participants with an adverse event.
• "total": total columns.
• "diff": risk difference.
• "diff_ci": 95% confidence interval for the risk difference using the Miettinen and Nurminen method.
• "diff_p": p-value for the risk difference using the Miettinen and Nurminen method.
• "dur": average adverse event duration.
• "events_avg": average number of adverse events per participant.
• "events_count": number of adverse events per participant.

The "diff_ci" and "diff_p" values require extend_ae_specific_inference(). The "dur" value requires extend_ae_specific_duration(), and the event statistics require extend_ae_specific_events().

Add a column for risk difference inference

The following example adds a 95% confidence interval and p-value based on the Miettinen and Nurminen method. See the rate comparison vignette for methodological details.

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

prepare_ae_specific(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_inference() |>
  format_ae_specific(
    display = c("n", "prop", "diff", "diff_ci", "diff_p")
  ) |>
  tlf_ae_specific(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_specific",
    meddra_version = "24.0",
    path_outtable = file.path(rtf_dir, "ae0specific2a.rtf")
  )
#> The output is saved in/home/runner/work/metalite.ae/metalite.ae/vignettes/rtf/ae0specific2a.rtf

Generated RTF file: ae0specific2a.rtf

Add a column for average event duration

Use extend_ae_specific_duration() to calculate the average duration of adverse events. The duration_var argument identifies the analysis variable that contains event duration.

prepare_ae_specific(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_duration(duration_var = "ADURN") |>
  format_ae_specific(display = c("n", "prop", "dur")) |>
  tlf_ae_specific(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_specific",
    meddra_version = "24.0",
    path_outtable = file.path(rtf_dir, "ae0specific2b.rtf")
  )
#> The output is saved in/home/runner/work/metalite.ae/metalite.ae/vignettes/rtf/ae0specific2b.rtf

Generated RTF file: ae0specific2b.rtf

Add a column for event frequency

Use extend_ae_specific_events() to add the event count and the average number of events per participant.

prepare_ae_specific(
  meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
) |>
  extend_ae_specific_events() |>
  format_ae_specific(
    display = c("n", "prop", "events_count", "events_avg")
  ) |>
  tlf_ae_specific(
    source = "Source:  [CDISCpilot: adam-adsl; adae]",
    analysis = "ae_specific",
    meddra_version = "24.0",
    path_outtable = file.path(rtf_dir, "ae0specific2c.rtf")
  )
#> The output is saved in/home/runner/work/metalite.ae/metalite.ae/vignettes/rtf/ae0specific2c.rtf

Generated RTF file: ae0specific2c.rtf