Filters outcome rows to analysis-set members. LOCF is included only for legacy and sensitivity workflows; it is generally unsuitable for primary confirmatory inference. Multiple imputation requires an explicit callback and never silently substitutes a single imputation.
Usage
analysisData(
set,
outcomes,
id_col = "id",
time_col = NULL,
value_cols = NULL,
missing = c("none", "locf", "multiple"),
imputer = NULL,
...
)Arguments
- set
An
analysis_set.- outcomes
Outcome data frame.
- id_col
Participant-ID column in
outcomes.- time_col
Optional time column, required for LOCF.
- value_cols
Columns subject to missing-data processing. Defaults to every non-ID/non-time column.
- missing
One of
"none","locf", or"multiple".- imputer
Explicit multiple-imputation callback.
- ...
Arguments forwarded to
imputer.
Value
A filtered data frame, or a non-empty list of completed data frames for a multiple-imputation callback.
Examples
trial <- Trial("T1", c("A", "B"))
p <- data.frame(
id = c("p1", "p2"), arm = c("A", "B"), randomized = TRUE
)
set <- intentionToTreat(trial, p)
outcomes <- data.frame(id = c("p1", "p1", "p2"), time = c(0, 1, 0),
value = c(NA, 2, 9))
analysisData(set, outcomes, time_col = "time", missing = "locf")
#> id time value
#> 1 p1 0 NA
#> 2 p1 1 2
#> 3 p2 0 9