Apply the composite ICE strategy: replace post-ICE outcomes with baseline

For subjects whose missingness flag matches composite_value, all missing post-ICE count observations are set to the subject's baseline count. This implements the composite estimand strategy for intercurrent events such as death or treatment discontinuation due to disease worsening, where the event itself is incorporated into the outcome (e.g., baseline carried forward as a "worst case" placeholder).

Usage

impute_nb_composite(
  data,
  outcome_col,
  imputed_value_col = "imputed_value",
  miss_flag_col,
  composite_value = "Comp",
  baseline_col
)

Arguments

data

Data frame.

outcome_col

Character. Column with the original count outcome; used to identify which rows are missing (NA).

imputed_value_col

Character. Column to update. If absent, it is created as a copy of outcome_col before the composite fill is applied. Default "imputed_value".

miss_flag_col

Character. Column with the missingness flag.

composite_value

Character. Flag value triggering the composite strategy. Default "Comp".

baseline_col

Character. Column with the baseline count used as the fill value.

Value

Data frame with imputed_value_col updated for composite rows.

Details

The function is intentionally simple and requires no model. It can be applied to a dataset already containing imputed_value from a prior MAR or MNAR imputation step, or directly to the original data.

Examples

df <- data.frame(
  count         = c(3L, NA,   NA,    5L),
  imputed_value = c(3L,  7L,  NA,    5L),
  miss_flag     = c(NA, "MAR", "Comp", NA),
  baseline      = c(4L,  4L,   4L,    6L)
)
impute_nb_composite(
  df,
  outcome_col     = "count",
  miss_flag_col   = "miss_flag",
  composite_value = "Comp",
  baseline_col    = "baseline"
)
#>   count imputed_value miss_flag baseline
#> 1     3             3      <NA>        4
#> 2    NA             7       MAR        4
#> 3    NA             4      Comp        4
#> 4     5             5      <NA>        6