Adverse event table — t_ae_pt_diff_slide • autoslider.core

Adverse event table

Usage

t_ae_pt_diff_slide(
  adsl,
  adae,
  arm = "TRT01A",
  cutoff = NA,
  split_by_study = FALSE,
  side_by_side = NULL
)

Arguments

adsl: ADSL data set, dataframe
adae: ADAE data set, dataframe
arm: Arm variable, character, "`TRT01A" by default.
cutoff: Cutoff threshold
split_by_study: Split by study, building structured header for tables
side_by_side: "GlobalAsia" or "GlobalAsiaChina" to define the side by side requirement

Value

rtables object

Note

* Default arm variables are set to `"TRT01A"` for safety output, and `"TRT01P"` for efficacy output

Examples

library(dplyr)
adsl <- eg_adsl %>%
  dplyr::mutate(TRT01A = factor(TRT01A, levels = c("A: Drug X", "B: Placebo")))
adae <- eg_adae %>%
  dplyr::mutate(
    TRT01A = factor(TRT01A, levels = c("A: Drug X", "B: Placebo")),
    ATOXGR = AETOXGR
  )
out <- t_ae_pt_diff_slide(adsl, adae, "TRT01A", 2)
print(out)
#> Adverse Events with Difference
#> 
#> ———————————————————————————————————————————————————————————————————————
#>    MedDRA Preferred Term N (%)   A: Drug X    B: Placebo   All Patients
#> ———————————————————————————————————————————————————————————————————————
#> dcd D.2.1.5.3                    37 (27.6%)   46 (34.3%)   133 (33.2%) 
#> dcd A.1.1.1.1                    45 (33.6%)   31 (23.1%)   128 (32.0%) 
#> dcd D.1.1.1.1                    42 (31.3%)   32 (23.9%)   120 (30.0%) 
#> dcd B.2.1.2.1                    39 (29.1%)   34 (25.4%)   119 (29.8%) 
#> dcd D.1.1.4.2                    38 (28.4%)   34 (25.4%)   112 (28.0%) 
#> dcd C.2.1.2.1                    28 (20.9%)   36 (26.9%)   112 (28.0%) 
generate_slides(out, paste0(tempdir(), "/ae_diff.pptx"))
#> [1] "Adverse Events with Difference"