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Impute missing counts under a reference-based MNAR assumption

Source: R/multiple_imputation_nb.R
impute_nb_mnar_ref.Rd

Implements the copy-reference (CR) strategy for observations whose missingness flag matches mnar_value in non-reference treatment arms. The imputation mean is the fixed-effects-only prediction under the reference arm, adjusted upward (or downward) by the subject's estimated random effect: $$\hat\mu_i^{\text{cf}} = \hat\mu_i^{\text{FE, ref}} \times \frac{\hat\mu_i^{\text{BLUP}}}{\hat\mu_i^{\text{FE}}}.$$ This mirrors the SAS PROC PLM approach that re-predicts under the counterfactual treatment and then multiplies by the BLUP ratio on the response scale.

Usage

impute_nb_mnar_ref(
  data,
  fits,
  outcome_col,
  miss_flag_col,
  mnar_value = "MNAR",
  trt_col,
  reference_trt,
  n_imp = 5L,
  replicate_col = NULL
)

Arguments

data

Data frame including all rows (observed and missing).

fits

Named list of fits as returned by fit_nb_glmm().

outcome_col

Character. Column with the count outcome.

miss_flag_col

Character. Column with the missingness flag.

mnar_value

Character. Flag value identifying MNAR rows. Default "MNAR".

trt_col

Character. Column with the treatment assignment.

reference_trt

Value in trt_col that identifies the reference arm.

n_imp

Integer. Number of imputations per replicate. Default 5.

replicate_col

Character or NULL. Replicate identifier column.

Value

Data frame in long format with all original columns plus imputation and imputed_value. Only MNAR non-reference rows have counterfactual imputations; all other rows pass through unchanged.

Details

MNAR subjects already in the reference arm should be handled by impute_nb_mar() (MAR imputation is appropriate for the reference arm because there is no better arm to "copy from").

Examples

if (FALSE) { # \dontrun{
fits     <- fit_nb_glmm(obs_data, count ~ base + trt + visit + (1 | id))
imp_mnar <- impute_nb_mnar_ref(
  data          = long_data,
  fits          = fits,
  outcome_col   = "count",
  miss_flag_col = "miss_flag",
  mnar_value    = "MNAR",
  trt_col       = "trt",
  reference_trt = 0L,
  n_imp         = 5L
)
} # }