Format AE specific analysis

Usage

format_ae_specific(
  outdata,
  display = c("n", "prop", "total"),
  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: Average number of adverse event per subject.

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

meta <- meta_ae_example()

outdata <- prepare_ae_specific(meta,
  population = "apat",
  observation = "wk12",
  parameter = "rel"
)

# Basic example
tbl <- outdata |>
  format_ae_specific()
head(tbl$tbl)
#>                                             name n_1 prop_1 n_2 prop_2 n_3
#> 1                     Participants in population  86   <NA>  84   <NA>  84
#> 2   with one or more drug-related adverse events  44 (51.2)  73 (86.9)  70
#> 3            with no drug-related adverse events  42 (48.8)  11 (13.1)  14
#> 4                                                 NA   <NA>  NA   <NA>  NA
#> 122                            Cardiac disorders   6  (7.0)   7  (8.3)   4
#> 25                           Atrial fibrillation   1  (1.2)   0  (0.0)   2
#>     prop_3 n_4 prop_4
#> 1     <NA> 254   <NA>
#> 2   (83.3) 187 (73.6)
#> 3   (16.7)  67 (26.4)
#> 4     <NA>  NA   <NA>
#> 122  (4.8)  17  (6.7)
#> 25   (2.4)   3  (1.2)

# Filtering
tbl <- outdata |>
  format_ae_specific(
    filter_method = "percent",
    filter_criteria = 10
  )
head(tbl$tbl)
#>                                                     name n_1 prop_1 n_2 prop_2
#> 1                             Participants in population  86   <NA>  84   <NA>
#> 2           with one or more drug-related adverse events  44 (51.2)  73 (86.9)
#> 3                    with no drug-related adverse events  42 (48.8)  11 (13.1)
#> 4                                                         NA   <NA>  NA   <NA>
#> 126                           Gastrointestinal disorders   4  (4.7)   8  (9.5)
#> 127 General disorders and administration site conditions  18 (20.9)  43 (51.2)
#>     n_3 prop_3 n_4 prop_4
#> 1    84   <NA> 254   <NA>
#> 2    70 (83.3) 187 (73.6)
#> 3    14 (16.7)  67 (26.4)
#> 4    NA   <NA>  NA   <NA>
#> 126  10 (11.9)  22  (8.7)
#> 127  35 (41.7)  96 (37.8)

# Display different measurements
tbl <- outdata |>
  extend_ae_specific_events() |>
  format_ae_specific(display = c("n", "prop", "events"))
head(tbl$tbl)
#>                                             name n_1 prop_1     events_1 n_2
#> 1                     Participants in population  86   <NA>           NA  84
#> 2   with one or more drug-related adverse events  44 (51.2)   0.7 ( 0.1)  73
#> 3            with no drug-related adverse events  42 (48.8)           NA  11
#> 4                                                 NA   <NA>           NA  NA
#> 122                            Cardiac disorders   6  (7.0)   2.3 ( 0.4)   7
#> 25                           Atrial fibrillation   1  (1.2)   1.0 ( 0.0)   0
#>     prop_2     events_2 n_3 prop_3     events_3
#> 1     <NA>           NA  84   <NA>           NA
#> 2   (86.9)   1.6 ( 0.2)  70 (83.3)   1.5 ( 0.2)
#> 3   (13.1)           NA  14 (16.7)           NA
#> 4     <NA>           NA  NA   <NA>           NA
#> 122  (8.3)   2.5 ( 0.3)   4  (4.8)   3.2 ( 0.3)
#> 25   (0.0)   1.7 ( 0.1)   2  (2.4)   1.5 ( 0.1)