Code
lyt <- basic_table(show_colcounts = TRUE) %>%
split_cols_by("ACTARM") %>%
analyze_num_patients(
vars = "USUBJID",
.stats = c("unique", "nonunique"),
.labels = c(
unique = "Total number of patients with at least one major protocol deviation related to epidemic/pandemic",
nonunique = "Total number of major protocol deviations related to epidemic/pandemic"
)
) %>%
split_rows_by(
"DVREAS",
split_fun = drop_split_levels,
nested = FALSE,
label_pos = "topleft",
split_label = obj_label(addv_pan$DVREAS)
) %>%
summarize_num_patients(
var = "USUBJID",
.stats = c("unique"),
.labels = "Site action due to epidemic/pandemic"
) %>%
count_occurrences(vars = "DVTERM") %>%
append_varlabels(addv_pan, "DVTERM", indent = 1L)
result <- build_table(lyt, addv_pan, alt_counts_df = adsl)
resultPrimary Reason A: Drug X B: Placebo C: Combination
Description (N=134) (N=134) (N=132)
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one major protocol deviation related to epidemic/pandemic 8 (6.0%) 4 (3.0%) 4 (3.0%)
Total number of major protocol deviations related to epidemic/pandemic 8 5 4
Site action due to epidemic/pandemic 8 (6.0%) 4 (3.0%) 4 (3.0%)
Dose missed or significantly out of window 2 (1.5%) 0 1 (0.8%)
Failure to sign updated ICF within two visits 2 (1.5%) 1 (0.7%) 1 (0.8%)
Missed 2 or more efficacy assessments 1 (0.7%) 2 (1.5%) 0
Significant deviation from planned dose 3 (2.2%) 1 (0.7%) 2 (1.5%)