The function ae_table_soc() creates a summary table of maximum AE grades for each patient according to term and SOC CTCAE.
The resulting dataframe can be piped to as_flextable() to get a nicely formatted flextable.
Usage
ae_table_soc(
df_ae,
...,
df_enrol,
arm = "ARM",
term = "AETERM",
soc = "AESOC",
grade = "AEGR",
subjid = "SUBJID",
sort_by_ae = TRUE,
total = TRUE,
digits = 0,
warn_miss = FALSE
)
# S3 method for class 'ae_table_soc'
as_flextable(x, arm_colors = c("#f2dcdb", "#dbe5f1", "#ebf1dd", "#e5e0ec"))Arguments
- df_ae
adverse event dataset, one row per AE, containing subjid, soc, and grade
- ...
unused
- df_enrol
enrollment dataset, one row per patient, containing subjid (and arm if needed). All patients should be in this dataset.
- arm
name of the treatment column in
df_enrol. Case-insensitive. Can be set toNULLto not group.- term
name of the the CTCAE term column in
df_ae. Case-insensitive. Can be set toNULL.- soc
name of the SOC column in
df_ae. Case-insensitive. Grade will be considered 0 if missing(e.g. if patient if absent fromdf_ae).- grade
name of the AE grade column in
df_ae. Case-insensitive.- subjid
name of the patient ID in both
df_aeanddf_enrol. Case-insensitive.- sort_by_ae
should the table be sorted by number or alphabetically
- total
whether to add a
totalcolumn for each arm- digits
significant digits for percentages
- warn_miss
whether to warn for missing values
- x
a dataframe, resulting of
ae_table_soc()- arm_colors
colors for the arm groups
See also
ae_table_grade_max(), ae_table_grade_n(), ae_table_soc(), ae_plot_grade_max(), ae_plot_grade_n()
Examples
tm = edc_example_ae()
#> Warning: Option "edc_lookup" has been overwritten.
ae_table_soc(df_ae=tm$ae, df_enrol=tm$enrolres, term=NULL)
#> # A tibble: 23 × 13
#> soc ctl_G1 ctl_G2 ctl_G3 ctl_G4 ctl_G5 ctl_Tot trt_G1 trt_G2 trt_G3 trt_G4
#> <chr> <glue> <glue> <glue> <glue> <glue> <glue> <glue> <glue> <glue> <glue>
#> 1 Injur… 1 (4%) NA 1 (4%) 1 (4%) NA 3 (11%) 3 (13… 1 (4%) 1 (4%) NA
#> 2 Neopl… 2 (7%) 1 (4%) NA NA NA 3 (11%) 2 (9%) NA 2 (9%) NA
#> 3 Nervo… 1 (4%) 1 (4%) 3 (11… NA NA 5 (19%) NA NA 3 (13… NA
#> 4 Eye d… 2 (7%) NA 2 (7%) 1 (4%) NA 5 (19%) 1 (4%) 1 (4%) NA NA
#> 5 Hepat… NA 2 (7%) NA 1 (4%) NA 3 (11%) 2 (9%) 1 (4%) 1 (4%) NA
#> 6 Infec… 2 (7%) 2 (7%) 1 (4%) NA NA 5 (19%) NA NA 1 (4%) NA
#> 7 Skin … NA NA 1 (4%) 1 (4%) NA 2 (7%) NA 2 (9%) 2 (9%) 1 (4%)
#> 8 Ear a… 1 (4%) NA NA NA NA 1 (4%) 1 (4%) 1 (4%) 1 (4%) 2 (9%)
#> 9 Repro… 2 (7%) 4 (15… NA NA NA 6 (22%) NA NA NA NA
#> 10 Respi… 2 (7%) NA 1 (4%) 1 (4%) NA 4 (15%) NA NA 2 (9%) NA
#> # ℹ 13 more rows
#> # ℹ 2 more variables: trt_G5 <glue>, trt_Tot <glue>
ae_table_soc(df_ae=tm$ae, df_enrol=tm$enrolres, term=NULL, arm=NULL)
#> # A tibble: 23 × 7
#> soc all_G1 all_G2 all_G3 all_G4 all_G5 all_Tot
#> <chr> <glue> <glue> <glue> <glue> <glue> <glue>
#> 1 Injury, poisoning and procedural … 4 (8%) 1 (2%) 2 (4%) 1 (2%) 1 (2%) 9 (18%)
#> 2 Neoplasms benign, malignant and u… 4 (8%) 1 (2%) 2 (4%) NA 1 (2%) 8 (16%)
#> 3 Nervous system disorders 1 (2%) 1 (2%) 6 (12… NA NA 8 (16%)
#> 4 Eye disorders 3 (6%) 1 (2%) 2 (4%) 1 (2%) NA 7 (14%)
#> 5 Hepatobiliary disorders 2 (4%) 3 (6%) 1 (2%) 1 (2%) NA 7 (14%)
#> 6 Infections and infestations 2 (4%) 2 (4%) 2 (4%) NA 1 (2%) 7 (14%)
#> 7 Skin and subcutaneous tissue diso… NA 2 (4%) 3 (6%) 2 (4%) NA 7 (14%)
#> 8 Ear and labyrinth disorders 2 (4%) 1 (2%) 1 (2%) 2 (4%) NA 6 (12%)
#> 9 Reproductive system and breast di… 2 (4%) 4 (8%) NA NA NA 6 (12%)
#> 10 Respiratory, thoracic and mediast… 2 (4%) NA 3 (6%) 1 (2%) NA 6 (12%)
#> # ℹ 13 more rows
if (FALSE) { # \dontrun{
#the resulting flextable can be customized using the flextable package
library(flextable)
ae_table_soc(tm$ae, df_enrol=tm$enrolres, total=FALSE) %>%
as_flextable() %>%
hline(i=~soc=="" & soc!=dplyr::lead(soc))
ae_table_soc(tm$ae, df_enrol=tm$enrolres, term=NULL, sort_by_ae=FALSE) %>%
as_flextable() %>%
hline()
ae_table_soc(tm$ae, df_enrol=tm$enrolres, term=NULL, arm=NULL) %>%
as_flextable()
} # }