Functions to score occurrence table subtables and rows which can be used in the sorting of occurrence tables.
![[Stable]](figures/lifecycle-stable.svg)
Usage
score_occurrences(table_row)
score_occurrences_cols(...)
score_occurrences_subtable(...)
score_occurrences_cont_cols(...)Arguments
- table_row
(
TableRow)
an analysis row in a occurrence table.- ...
arguments for row or column access, see
rtables_access: eithercol_names(character) including the names of the columns which should be used, or alternativelycol_indices(integer) giving the indices directly instead.
Value
score_occurrences() returns the sum of counts across all columns of a table row.
score_occurrences_cols() returns a function that sums counts across all specified columns
of a table row.
score_occurrences_subtable() returns a function that sums counts in each subtable
across all specified columns.
Functions
score_occurrences(): Scoring function which sums the counts across all columns.score_occurrences_cols(): Scoring functions can be produced by this constructor to only include specific columns in the scoring.score_occurrences_subtable(): Scoring functions produced by this constructor can be used on subtables: They sum up all specified column counts in the subtable. This is useful when there is no available content row summing up these counts.score_occurrences_cont_cols(): Produce score function for sorting table by summing the first content row in specified columns. Note that this is extendingrtables::cont_n_onecol()andrtables::cont_n_allcols().
Examples
library(scda)
library(rtables)
adsl <- synthetic_cdisc_data("latest")$adsl
adae <- synthetic_cdisc_data("latest")$adae
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
add_colcounts() %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique"),
.labels = c("Total number of patients with at least one event")
) %>%
split_rows_by("AEBODSYS", child_labels = "visible", nested = FALSE) %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
"Total number of patients with at least one event",
"Total number of events"
)
) %>%
count_occurrences(vars = "AEDECOD")
rtable_object <- build_table(lyt, adae, alt_counts_df = adsl) %>%
prune_table()
rtable_object_sorted <- rtable_object %>%
sort_at_path(path = c("AEBODSYS", "*", "AEDECOD"), scorefun = score_occurrences)
rtable_object_sorted
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one event 122 (91%) 123 (91.8%) 120 (90.9%)
#> cl A.1
#> Total number of patients with at least one event 78 (58.2%) 75 (56%) 89 (67.4%)
#> Total number of events 132 130 160
#> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%)
#> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%)
#> cl B.1
#> Total number of patients with at least one event 47 (35.1%) 49 (36.6%) 43 (32.6%)
#> Total number of events 56 60 62
#> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%)
#> cl B.2
#> Total number of patients with at least one event 79 (59%) 74 (55.2%) 85 (64.4%)
#> Total number of events 129 138 143
#> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%)
#> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%)
#> cl C.1
#> Total number of patients with at least one event 43 (32.1%) 46 (34.3%) 43 (32.6%)
#> Total number of events 55 63 64
#> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%)
#> cl C.2
#> Total number of patients with at least one event 35 (26.1%) 48 (35.8%) 55 (41.7%)
#> Total number of events 48 53 65
#> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%)
#> cl D.1
#> Total number of patients with at least one event 79 (59%) 67 (50%) 80 (60.6%)
#> Total number of events 127 106 135
#> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%)
#> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%)
#> cl D.2
#> Total number of patients with at least one event 47 (35.1%) 58 (43.3%) 57 (43.2%)
#> Total number of events 62 72 74
#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%)
score_cols_a_and_b <- score_occurrences_cols(col_names = c("A: Drug X", "B: Placebo"))
# Note that this here just sorts the AEDECOD inside the AEBODSYS. The AEBODSYS are not sorted.
# That would require a second pass of `sort_at_path`.
rtable_object_sorted <- rtable_object %>%
sort_at_path(path = c("AEBODSYS", "*", "AEDECOD"), scorefun = score_cols_a_and_b)
rtable_object_sorted
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one event 122 (91%) 123 (91.8%) 120 (90.9%)
#> cl A.1
#> Total number of patients with at least one event 78 (58.2%) 75 (56%) 89 (67.4%)
#> Total number of events 132 130 160
#> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%)
#> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%)
#> cl B.1
#> Total number of patients with at least one event 47 (35.1%) 49 (36.6%) 43 (32.6%)
#> Total number of events 56 60 62
#> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%)
#> cl B.2
#> Total number of patients with at least one event 79 (59%) 74 (55.2%) 85 (64.4%)
#> Total number of events 129 138 143
#> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%)
#> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%)
#> cl C.1
#> Total number of patients with at least one event 43 (32.1%) 46 (34.3%) 43 (32.6%)
#> Total number of events 55 63 64
#> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%)
#> cl C.2
#> Total number of patients with at least one event 35 (26.1%) 48 (35.8%) 55 (41.7%)
#> Total number of events 48 53 65
#> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%)
#> cl D.1
#> Total number of patients with at least one event 79 (59%) 67 (50%) 80 (60.6%)
#> Total number of events 127 106 135
#> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%)
#> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%)
#> cl D.2
#> Total number of patients with at least one event 47 (35.1%) 58 (43.3%) 57 (43.2%)
#> Total number of events 62 72 74
#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%)
score_subtable_all <- score_occurrences_subtable(col_names = names(rtable_object))
# Note that this code just sorts the AEBODSYS, not the AEDECOD within AEBODSYS. That
# would require a second pass of `sort_at_path`.
rtable_object_sorted <- rtable_object %>%
sort_at_path(path = c("AEBODSYS"), scorefun = score_subtable_all, decreasing = FALSE)
rtable_object_sorted
#> A: Drug X B: Placebo C: Combination
#> (N=134) (N=134) (N=132)
#> ————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one event 122 (91%) 123 (91.8%) 120 (90.9%)
#> cl C.2
#> Total number of patients with at least one event 35 (26.1%) 48 (35.8%) 55 (41.7%)
#> Total number of events 48 53 65
#> dcd C.2.1.2.1 35 (26.1%) 48 (35.8%) 55 (41.7%)
#> cl C.1
#> Total number of patients with at least one event 43 (32.1%) 46 (34.3%) 43 (32.6%)
#> Total number of events 55 63 64
#> dcd C.1.1.1.3 43 (32.1%) 46 (34.3%) 43 (32.6%)
#> cl B.1
#> Total number of patients with at least one event 47 (35.1%) 49 (36.6%) 43 (32.6%)
#> Total number of events 56 60 62
#> dcd B.1.1.1.1 47 (35.1%) 49 (36.6%) 43 (32.6%)
#> cl D.2
#> Total number of patients with at least one event 47 (35.1%) 58 (43.3%) 57 (43.2%)
#> Total number of events 62 72 74
#> dcd D.2.1.5.3 47 (35.1%) 58 (43.3%) 57 (43.2%)
#> cl D.1
#> Total number of patients with at least one event 79 (59%) 67 (50%) 80 (60.6%)
#> Total number of events 127 106 135
#> dcd D.1.1.1.1 50 (37.3%) 42 (31.3%) 51 (38.6%)
#> dcd D.1.1.4.2 48 (35.8%) 42 (31.3%) 50 (37.9%)
#> cl B.2
#> Total number of patients with at least one event 79 (59%) 74 (55.2%) 85 (64.4%)
#> Total number of events 129 138 143
#> dcd B.2.1.2.1 49 (36.6%) 44 (32.8%) 52 (39.4%)
#> dcd B.2.2.3.1 48 (35.8%) 54 (40.3%) 51 (38.6%)
#> cl A.1
#> Total number of patients with at least one event 78 (58.2%) 75 (56%) 89 (67.4%)
#> Total number of events 132 130 160
#> dcd A.1.1.1.1 50 (37.3%) 45 (33.6%) 63 (47.7%)
#> dcd A.1.1.1.2 48 (35.8%) 48 (35.8%) 50 (37.9%)