![[Stable]](figures/lifecycle-stable.svg)
Functions to score occurrence table subtables and rows which can be used in the sorting of occurrence tables.
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: either
col_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.
score_occurrences_cont_cols()returns a function that sums counts in the first content row in specified columns.
Functions
score_occurrences(): Scoring function which sums the counts across all columns. It will fail if anything else but counts are used.score_occurrences_cols(): Scoring functions can be produced by this constructor to only include specific columns in the scoring. Seeh_row_counts()for further information.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
lyt <- basic_table() %>%
split_cols_by("ARM") %>%
add_colcounts() %>%
analyze_num_patients(
vars = "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")
tbl <- build_table(lyt, tern_ex_adae, alt_counts_df = tern_ex_adsl) %>%
prune_table()
tbl_sorted <- tbl %>%
sort_at_path(path = c("AEBODSYS", "*", "AEDECOD"), scorefun = score_occurrences)
tbl_sorted
#> A: Drug X B: Placebo C: Combination
#> (N=69) (N=73) (N=58)
#> —————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%)
#> cl D.1
#> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%)
#> Total number of events 39 35 28
#> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%)
#> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%)
#> cl C.1
#> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%)
#> Total number of events 17 15 19
#> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%)
#> cl C.2
#> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%)
#> Total number of events 21 15 14
#> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%)
#> cl B.2
#> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%)
#> Total number of events 40 38 33
#> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%)
#> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%)
#> cl B.1
#> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%)
#> Total number of events 19 19 18
#> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%)
#> cl D.2
#> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%)
#> Total number of events 27 22 15
#> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%)
#> cl A.1
#> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%)
#> Total number of events 39 33 35
#> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%)
#> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%)
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`.
tbl_sorted <- tbl %>%
sort_at_path(path = c("AEBODSYS", "*", "AEDECOD"), scorefun = score_cols_a_and_b)
tbl_sorted
#> A: Drug X B: Placebo C: Combination
#> (N=69) (N=73) (N=58)
#> —————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%)
#> cl D.1
#> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%)
#> Total number of events 39 35 28
#> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%)
#> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%)
#> cl C.1
#> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%)
#> Total number of events 17 15 19
#> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%)
#> cl C.2
#> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%)
#> Total number of events 21 15 14
#> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%)
#> cl B.2
#> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%)
#> Total number of events 40 38 33
#> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%)
#> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%)
#> cl B.1
#> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%)
#> Total number of events 19 19 18
#> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%)
#> cl D.2
#> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%)
#> Total number of events 27 22 15
#> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%)
#> cl A.1
#> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%)
#> Total number of events 39 33 35
#> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%)
#> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%)
score_subtable_all <- score_occurrences_subtable(col_names = names(tbl))
# Note that this code just sorts the AEBODSYS, not the AEDECOD within AEBODSYS. That
# would require a second pass of `sort_at_path`.
tbl_sorted <- tbl %>%
sort_at_path(path = c("AEBODSYS"), scorefun = score_subtable_all, decreasing = FALSE)
tbl_sorted
#> A: Drug X B: Placebo C: Combination
#> (N=69) (N=73) (N=58)
#> —————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one event 59 (85.5%) 57 (78.1%) 48 (82.8%)
#> cl C.2
#> Total number of patients with at least one event 20 (29.0%) 14 (19.2%) 10 (17.2%)
#> Total number of events 21 15 14
#> dcd C.2.1.2.1 20 (29.0%) 14 (19.2%) 10 (17.2%)
#> cl C.1
#> Total number of patients with at least one event 15 (21.7%) 13 (17.8%) 18 (31.0%)
#> Total number of events 17 15 19
#> dcd C.1.1.1.3 15 (21.7%) 13 (17.8%) 18 (31.0%)
#> cl B.1
#> Total number of patients with at least one event 15 (21.7%) 19 (26.0%) 15 (25.9%)
#> Total number of events 19 19 18
#> dcd B.1.1.1.1 15 (21.7%) 19 (26.0%) 15 (25.9%)
#> cl D.2
#> Total number of patients with at least one event 21 (30.4%) 20 (27.4%) 12 (20.7%)
#> Total number of events 27 22 15
#> dcd D.2.1.5.3 21 (30.4%) 20 (27.4%) 12 (20.7%)
#> cl D.1
#> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 20 (34.5%)
#> Total number of events 39 35 28
#> dcd D.1.1.1.1 17 (24.6%) 18 (24.7%) 7 (12.1%)
#> dcd D.1.1.4.2 16 (23.2%) 13 (17.8%) 16 (27.6%)
#> cl B.2
#> Total number of patients with at least one event 29 (42.0%) 27 (37.0%) 24 (41.4%)
#> Total number of events 40 38 33
#> dcd B.2.1.2.1 17 (24.6%) 16 (21.9%) 13 (22.4%)
#> dcd B.2.2.3.1 17 (24.6%) 15 (20.5%) 16 (27.6%)
#> cl A.1
#> Total number of patients with at least one event 31 (44.9%) 24 (32.9%) 27 (46.6%)
#> Total number of events 39 33 35
#> dcd A.1.1.1.1 17 (24.6%) 17 (23.3%) 14 (24.1%)
#> dcd A.1.1.1.2 17 (24.6%) 14 (19.2%) 17 (29.3%)