Adverse event summary table
Usage
t_ae_summ_slide(
adsl,
adae,
arm = "TRT01A",
dose_adjust_flags = NA,
dose_adjust_labels = NA,
gr34_highest_grade_only = TRUE
)Arguments
- adsl
ADSL dataset, dataframe
- adae
ADAE dataset, dataframe
- arm
Arm variable, character, "`TRT01A" by default.
- dose_adjust_flags
Character or a vector of characters. Each character is a variable name in adae dataset. These variables are Logical vectors which flag AEs leading to dose adjustment, such as drug discontinuation, dose interruption and reduction. The flag can be related to any drug, or a specific drug.
- dose_adjust_labels
Character or a vector of characters. Each character represents a label displayed in the AE summary table (e.g. AE leading to discontinuation from drug X). The order of the labels should match the order of variable names in
dose_adjust_flags.- gr34_highest_grade_only
A logical value. Default is TRUE, such that only patients with the highest AE grade as 3 or 4 are included for the count of the "Grade 3-4 AE" and "Treatment-related Grade 3-4 AE" ; set it to FALSE if you want to include patients with the highest AE grade as 5.
Examples
library(dplyr)
ADSL <- eg_adsl
ADAE <- eg_adae
ADAE <- ADAE %>%
dplyr::mutate(ATOXGR = AETOXGR)
t_ae_summ_slide(adsl = ADSL, adae = ADAE)
#> Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
#> occured at (row) path: root
#> AE summary table
#>
#> ———————————————————————————————————————————————————————————————————————————————————
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ———————————————————————————————————————————————————————————————————————————————————
#> All grade AEs, any cause 100 (74.6%) 98 (73.1%) 103 (78.0%) 301 (75.2%)
#> Related 86 (64.2%) 85 (63.4%) 92 (69.7%) 263 (65.8%)
#> Grade 3-4 AEs 26 (19.4%) 31 (23.1%) 29 (22.0%) 86 (21.5%)
#> Related 13 (9.7%) 18 (13.4%) 15 (11.4%) 46 (11.5%)
#> Grade 5 AE 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
#> Related 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
#> SAEs 85 (63.4%) 80 (59.7%) 87 (65.9%) 252 (63.0%)
#> Related 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
# add flag for ae leading to dose reduction
ADAE$reduce_flg <- ifelse(ADAE$AEACN == "DOSE REDUCED", TRUE, FALSE)
t_ae_summ_slide(
adsl = ADSL, adae = ADAE,
dose_adjust_flags = c("reduce_flg"),
dose_adjust_labels = c("AE leading to dose reduction of drug X")
)
#> Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
#> occured at (row) path: root
#> AE summary table
#>
#> —————————————————————————————————————————————————————————————————————————————————————————————————
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> —————————————————————————————————————————————————————————————————————————————————————————————————
#> All grade AEs, any cause 100 (74.6%) 98 (73.1%) 103 (78.0%) 301 (75.2%)
#> Related 86 (64.2%) 85 (63.4%) 92 (69.7%) 263 (65.8%)
#> Grade 3-4 AEs 26 (19.4%) 31 (23.1%) 29 (22.0%) 86 (21.5%)
#> Related 13 (9.7%) 18 (13.4%) 15 (11.4%) 46 (11.5%)
#> Grade 5 AE 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
#> Related 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
#> SAEs 85 (63.4%) 80 (59.7%) 87 (65.9%) 252 (63.0%)
#> Related 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
#> AE leading to dose reduction of drug X 41 (30.6%) 37 (27.6%) 43 (32.6%) 121 (30.2%)
# add flgs for ae leading to dose reduction, drug withdraw and drug interruption
ADAE$withdraw_flg <- ifelse(ADAE$AEACN == "DRUG WITHDRAWN", TRUE, FALSE)
ADAE$interrup_flg <- ifelse(ADAE$AEACN == "DRUG INTERRUPTED", TRUE, FALSE)
out <- t_ae_summ_slide(
adsl = ADSL, adae = ADAE, arm = "TRT01A",
dose_adjust_flags = c("withdraw_flg", "reduce_flg", "interrup_flg"),
dose_adjust_labels = c(
"AE leading to discontinuation from drug X",
"AE leading to drug X reduction",
"AE leading to drug X interruption"
)
)
#> Warning: Non-unique sibling analysis table names. Using Labels instead. Use the table_names argument to analyze to avoid this when analyzing the same variable multiple times.
#> occured at (row) path: root
print(out)
#> AE summary table
#>
#> ————————————————————————————————————————————————————————————————————————————————————————————————————
#> A: Drug X B: Placebo C: Combination All Patients
#> (N=134) (N=134) (N=132) (N=400)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————
#> All grade AEs, any cause 100 (74.6%) 98 (73.1%) 103 (78.0%) 301 (75.2%)
#> Related 86 (64.2%) 85 (63.4%) 92 (69.7%) 263 (65.8%)
#> Grade 3-4 AEs 26 (19.4%) 31 (23.1%) 29 (22.0%) 86 (21.5%)
#> Related 13 (9.7%) 18 (13.4%) 15 (11.4%) 46 (11.5%)
#> Grade 5 AE 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
#> Related 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
#> SAEs 85 (63.4%) 80 (59.7%) 87 (65.9%) 252 (63.0%)
#> Related 64 (47.8%) 52 (38.8%) 64 (48.5%) 180 (45.0%)
#> AE leading to discontinuation from drug X 22 (16.4%) 21 (15.7%) 28 (21.2%) 71 (17.8%)
#> AE leading to drug X reduction 41 (30.6%) 37 (27.6%) 43 (32.6%) 121 (30.2%)
#> AE leading to drug X interruption 4 (3.0%) 4 (3.0%) 3 (2.3%) 11 (2.8%)
generate_slides(out, paste0(tempdir(), "/ae_summary.pptx"))
#> [1] "AE summary table"
#> [1] "AE summary table (cont.)"