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Format AE summary analysis

Usage

format_ae_summary(
  outdata,
  display = c("n", "prop", "total"),
  hide_soc_stats = FALSE,
  digits_prop = 1,
  digits_ci = 1,
  digits_p = 3,
  digits_dur = c(1, 1),
  digits_events = c(1, 1),
  filter_method = c("percent", "count"),
  filter_criteria = 0,
  sort_order = c("alphabetical", "count_des", "count_asc"),
  sort_column = NULL,
  mock = FALSE
)

Arguments

outdata

An outdata object created by prepare_ae_specific().

display

A character vector of measurement to be displayed:

  • n: Number of subjects with adverse event.

  • prop: Proportion of subjects with adverse event.

  • total: Total columns.

  • diff: Risk difference.

  • diff_ci: 95% confidence interval of risk difference using M&N method.

  • diff_p: p-value of risk difference using M&N method.

  • dur: Average of adverse event duration.

  • events_avg: Average number of adverse event per subject.

  • events_count: Count number of adverse event per subject.

hide_soc_stats

A boolean value to hide stats for SOC rows.

digits_prop

A numeric value of number of digits for proportion value.

digits_ci

A numeric value of number of digits for confidence interval.

digits_p

A numeric value of number of digits for p-value.

digits_dur

A numeric value of number of digits for average duration of adverse event.

digits_events

A numeric value of number of digits for average of number of adverse events per subject.

filter_method

A character value to specify how to filter rows:

  • count: Filtered based on participant count.

  • percent: Filtered based percent incidence.

filter_criteria

A numeric value to display rows where at least one therapy group has a percent incidence or participant count greater than or equal to the specified value. If filter_method is percent, the value should be between 0 and 100. If filter_method is count, the value should be greater than 0.

sort_order

A character value to specify sorting order:

  • alphabetical: Sort by alphabetical order.

  • count_des: Sort by count in descending order.

  • count_asc: Sort by count in ascending order.

sort_column

A character value of group in outdata used to sort a table with.

mock

A boolean value to display mock table.

Value

A list of analysis raw datasets.

Examples

# 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()

outdata <- prepare_ae_summary(meta,
  population = "apat",
  observation = "wk12",
  parameter = "any;rel;ser"
)
#> any
#> rel
#> ser
tbl <- outdata |>
  format_ae_summary()
head(tbl$tbl)
#>                                    name n_1 prop_1 n_2 prop_2 n_3 prop_3
#> 1            Participants in population  84   <NA>  86   <NA> 170   <NA>
#> 2       with one or more adverse events  77 (91.7)  69 (80.2) 146 (85.9)
#> 3                with no adverse events   7  (8.3)  17 (19.8)  24 (14.1)
#> 21 with drug-related{^a} adverse events  73 (86.9)  44 (51.2) 117 (68.8)
#> 22          with serious adverse events   1  (1.2)   0  (0.0)   1  (0.6)