A function used for sorting AE tables (and others) as required.
Usage
jj_complex_scorefun(
spanningheadercolvar = "colspan_trt",
usefirstcol = FALSE,
colpath = NULL,
firstcat = NULL,
lastcat = NULL
)Arguments
- spanningheadercolvar
name of spanning header variable that defines the active treatment columns. If you do not have an active treatment spanning header column then user can define this as NA.
- usefirstcol
This allows you to just use the first column of the table to sort on.
- colpath
name of column path that is needed to sort by (default=NULL). This overrides other arguments if specified (except firstcat and lastcat which will be applied if requested on this colpath)
- firstcat
If you wish to put any category at the top of the list despite any n's user can specify here.
- lastcat
If you wish to put any category at the bottom of the list despite any n's user can specify here.
Details
This sort function sorts as follows: Takes all the columns from a specified spanning column header (default= colspan_trt) and sorts by the last treatment column within this. If no spanning column header variable exists (e.g you have only one active treatment arm and have decided to remove the spanning header from your layout) it will sort by the first treatment column in your table. This function is not really designed for tables that have sub-columns, however if users wish to override any default sorting behavior, they can simply specify their own colpath to use for sorting on (default=NULL)
Examples
ADAE <- data.frame(
USUBJID = c(
'XXXXX01', 'XXXXX02', 'XXXXX03', 'XXXXX04', 'XXXXX05',
'XXXXX06', 'XXXXX07', 'XXXXX08', 'XXXXX09', 'XXXXX10'
),
AEBODSYS = c(
'SOC 1', 'SOC 2', 'SOC 1', 'SOC 2', 'SOC 2',
'SOC 2', 'SOC 2', 'SOC 1', 'SOC 2', 'SOC 1'
),
AEDECOD = c(
'Coded Term 2', 'Coded Term 1', 'Coded Term 3', 'Coded Term 4',
'Coded Term 4', 'Coded Term 4', 'Coded Term 5', 'Coded Term 3',
'Coded Term 1', 'Coded Term 2'
),
TRT01A = c(
'ARMA', 'ARMB', 'ARMA', 'ARMB', 'ARMB',
'Placebo', 'Placebo', 'Placebo', 'ARMA', 'ARMB'
),
TRTEMFL = c('Y', 'Y', 'N', 'Y', 'Y', 'Y', 'Y', 'N', 'Y', 'Y')
)
ADAE <- ADAE |>
dplyr::mutate(TRT01A = as.factor(TRT01A))
ADAE$colspan_trt <- factor(ifelse(ADAE$TRT01A == 'Placebo', ' ', 'Active Study Agent'),
levels = c('Active Study Agent', ' ')
)
ADAE$rrisk_header <- 'Risk Difference (%) (95% CI)'
ADAE$rrisk_label <- paste(ADAE$TRT01A, paste('vs', 'Placebo'))
colspan_trt_map <- create_colspan_map(ADAE,
non_active_grp = 'Placebo',
non_active_grp_span_lbl = ' ',
active_grp_span_lbl = 'Active Study Agent',
colspan_var = 'colspan_trt',
trt_var = 'TRT01A'
)
ref_path <- c("colspan_trt", " ", "TRT01A", "Placebo")
lyt <- basic_table() |>
split_cols_by(
'colspan_trt',
split_fun = trim_levels_to_map(map = colspan_trt_map)
) |>
split_cols_by('TRT01A') |>
split_cols_by('rrisk_header', nested = FALSE) |>
split_cols_by(
'TRT01A',
labels_var = 'rrisk_label',
split_fun = remove_split_levels('Placebo')
) |>
analyze(
'TRTEMFL',
a_freq_j,
show_labels = 'hidden',
extra_args = list(
method = 'wald',
label = 'Subjects with >=1 AE',
ref_path = ref_path,
.stats = "count_unique_fraction"
)
) |>
split_rows_by('AEBODSYS',
split_label = 'System Organ Class',
split_fun = trim_levels_in_group('AEDECOD'),
label_pos = 'topleft',
section_div = c(' '),
nested = FALSE
) |>
summarize_row_groups(
'AEBODSYS',
cfun = a_freq_j,
extra_args = list(
method = 'wald',
ref_path = ref_path,
.stats = "count_unique_fraction"
)
) |>
analyze(
'AEDECOD',
afun = a_freq_j,
extra_args = list(
method = 'wald',
ref_path = ref_path,
.stats = "count_unique_fraction"
)
)
result <- build_table(lyt, ADAE)
result
#> Active Study Agent Risk Difference (%) (95% CI)
#> System Organ Class ARMA ARMB Placebo ARMA vs Placebo ARMB vs Placebo
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Subjects with >=1 AE 3 (100.0%) 4 (100.0%) 3 (100.0%) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0)
#> SOC 1 2 (66.7%) 1 (25.0%) 1 (33.3%) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0)
#> Coded Term 2 1 (33.3%) 1 (25.0%) 0 50.0 (-19.3, 100.0) 100.0 (100.0, 100.0)
#> Coded Term 3 1 (33.3%) 0 1 (33.3%) -50.0 (-100.0, 19.3) -100.0 (-100.0, -100.0)
#>
#> SOC 2 1 (33.3%) 3 (75.0%) 2 (66.7%) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0)
#> Coded Term 1 1 (33.3%) 1 (25.0%) 0 100.0 (100.0, 100.0) 33.3 (-20.0, 86.7)
#> Coded Term 4 0 2 (50.0%) 1 (33.3%) -50.0 (-100.0, 19.3) 16.7 (-70.8, 100.0)
#> Coded Term 5 0 0 1 (33.3%) -50.0 (-100.0, 19.3) -50.0 (-100.0, 19.3)
# now add sorting function to sort by AEBODSYS and AEDECOD within SOC
# using complex score function which sorts
# using the last active treatment column by default
result <- sort_at_path(
result,
c('root', 'AEBODSYS'),
scorefun = jj_complex_scorefun()
)
result <- sort_at_path(
result,
c('root', 'AEBODSYS', '*', 'AEDECOD'),
scorefun = jj_complex_scorefun()
)
result
#> Active Study Agent Risk Difference (%) (95% CI)
#> System Organ Class ARMA ARMB Placebo ARMA vs Placebo ARMB vs Placebo
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Subjects with >=1 AE 3 (100.0%) 4 (100.0%) 3 (100.0%) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0)
#> SOC 2 1 (33.3%) 3 (75.0%) 2 (66.7%) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0)
#> Coded Term 4 0 2 (50.0%) 1 (33.3%) -50.0 (-100.0, 19.3) 16.7 (-70.8, 100.0)
#> Coded Term 1 1 (33.3%) 1 (25.0%) 0 100.0 (100.0, 100.0) 33.3 (-20.0, 86.7)
#> Coded Term 5 0 0 1 (33.3%) -50.0 (-100.0, 19.3) -50.0 (-100.0, 19.3)
#>
#> SOC 1 2 (66.7%) 1 (25.0%) 1 (33.3%) 0.0 (0.0, 0.0) 0.0 (0.0, 0.0)
#> Coded Term 2 1 (33.3%) 1 (25.0%) 0 50.0 (-19.3, 100.0) 100.0 (100.0, 100.0)
#> Coded Term 3 1 (33.3%) 0 1 (33.3%) -50.0 (-100.0, 19.3) -100.0 (-100.0, -100.0)