Storing and Analyzing Imputed Data with rbmiUtils

2026-02-16

1 Introduction

This vignette demonstrates how to:

This pattern enables reproducible workflows where imputation and analysis can be separated and revisited independently.

Related vignettes:

2 Statistical Context

This approach applies Rubin’s Rules for inference after multiple imputation (see the rbmi quickstart vignette for background on the draws/impute/analyse/pool pipeline):

We fit a model to each imputed dataset, derive a response variable on the CHG score, extract marginal effects or other statistics of interest, and combine the results into a single inference using Rubin’s combining rules.

3 Step 1: Setup and Data Preparation

library(dplyr)
library(tidyr)
library(readr)
library(purrr)
library(rbmi)
library(beeca)
library(rbmiUtils)
set.seed(1974)
data("ADEFF")

ADEFF <- ADEFF %>%
  mutate(
    TRT = factor(TRT01P, levels = c("Placebo", "Drug A")),
    USUBJID = factor(USUBJID),
    AVISIT = factor(AVISIT)
  )

4 Step 2: Define Imputation Model

We use rbmi::set_vars() to specify the key variable roles:

vars <- set_vars(
  subjid = "USUBJID",
  visit = "AVISIT",
  group = "TRT",
  outcome = "CHG",
  covariates = c("BASE", "STRATA", "REGION")
)
method <- method_bayes(
  n_samples = 100,
  control = control_bayes(warmup = 200, thin = 2)
)
dat <- ADEFF %>%
  select(USUBJID, STRATA, REGION, REGIONC, TRT, BASE, CHG, AVISIT)

draws_obj <- draws(data = dat, vars = vars, method = method)
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impute_obj <- impute(draws_obj, references = c("Placebo" = "Placebo", "Drug A" = "Placebo"))

ADMI <- get_imputed_data(impute_obj)

5 Step 3: Add Responder Variables

ADMI <- ADMI %>%
  mutate(
    CRIT1FLN = ifelse(CHG > 3, 1, 0),
    CRIT1FL = ifelse(CRIT1FLN == 1, "Y", "N"),
    CRIT = "CHG > 3"
  )

6 Step 4: Continuous Endpoint Analysis (CHG)

ana_obj_ancova <- analyse_mi_data(
  data = ADMI,
  vars = vars,
  method = method,
  fun = ancova
)
pool_obj_ancova <- pool(ana_obj_ancova)
print(pool_obj_ancova)
#>        parameter   visit   est   lci   uci    pval
#>      trt_Week 24 Week 24 -2.18 -2.53 -1.82 < 0.001
#>  lsm_ref_Week 24 Week 24  0.08 -0.18  0.33   0.560
#>  lsm_alt_Week 24 Week 24 -2.10 -2.35 -1.85 < 0.001
#>      trt_Week 48 Week 48 -3.81 -4.31 -3.30 < 0.001
#>  lsm_ref_Week 48 Week 48  0.04 -0.32  0.41   0.814
#>  lsm_alt_Week 48 Week 48 -3.76 -4.11 -3.42 < 0.001
tidy_pool_obj(pool_obj_ancova)
#> # A tibble: 6 × 10
#>   parameter       description visit parameter_type lsm_type     est    se    lci
#>   <chr>           <chr>       <chr> <chr>          <chr>      <dbl> <dbl>  <dbl>
#> 1 trt_Week 24     Treatment … Week… trt            <NA>     -2.18   0.182 -2.53 
#> 2 lsm_ref_Week 24 Least Squa… Week… lsm            ref       0.0764 0.131 -0.181
#> 3 lsm_alt_Week 24 Least Squa… Week… lsm            alt      -2.10   0.125 -2.35 
#> 4 trt_Week 48     Treatment … Week… trt            <NA>     -3.81   0.256 -4.31 
#> 5 lsm_ref_Week 48 Least Squa… Week… lsm            ref       0.0436 0.185 -0.320
#> 6 lsm_alt_Week 48 Least Squa… Week… lsm            alt      -3.76   0.175 -4.11 
#> # ℹ 2 more variables: uci <dbl>, pval <dbl>

7 Step 5: Responder Endpoint Analysis (CRIT1FLN)

7.1 Define Analysis Function

We use beeca::get_marginal_effect() for robust variance estimation of marginal treatment effects from the logistic model:

gcomp_responder <- function(data, ...) {
  model <- glm(CRIT1FLN ~ TRT + BASE + STRATA + REGION, data = data, family = binomial)

  marginal_fit <- get_marginal_effect(
    model,
    trt = "TRT",
    method = "Ge",
    type = "HC0",
    contrast = "diff",
    reference = "Placebo"
  )

  res <- marginal_fit$marginal_results
  list(
    trt = list(
      est = res[res$STAT == "diff", "STATVAL"][[1]],
      se = res[res$STAT == "diff_se", "STATVAL"][[1]],
      df = NA
    )
  )
}

7.2 Define Variables and Run Analysis

vars_binary <- set_vars(
  subjid = "USUBJID",
  visit = "AVISIT",
  group = "TRT",
  outcome = "CRIT1FLN",
  covariates = c("BASE", "STRATA", "REGION")
)
ana_obj_prop <- analyse_mi_data(
  data = ADMI,
  vars = vars_binary,
  method = method,
  fun = gcomp_responder
)
pool_obj_prop <- pool(ana_obj_prop)
print(pool_obj_prop)
#>  parameter visit   est   lci   uci    pval
#>        trt  <NA> -0.06 -0.09 -0.04 < 0.001

8 Final Notes

8.1 Efficient Storage

When working with many imputations, consider using reduce_imputed_data() to store only the imputed values:

# Reduce for efficient storage
reduced <- reduce_imputed_data(ADMI, ADEFF, vars)

# Later, expand back for analysis
ADMI_restored <- expand_imputed_data(reduced, ADEFF, vars)

See the Efficient Storage vignette for details.

8.2 See Also

For a guided tutorial walking through the complete pipeline from raw data to regulatory tables, see vignette('pipeline').