Convert Pool Object to ARD Format

Converts an rbmi pool object to the pharmaverse Analysis Results Dataset (ARD) standard using the cards package. The ARD format is a long-format data frame where each row represents a single statistic for a given parameter, with grouping columns for visit, parameter type, and least-squares-mean type.

Usage

pool_to_ard(pool_obj, analysis_obj = NULL, conf.level = NULL)

Arguments

pool_obj

A pooled analysis object of class "pool", typically obtained from rbmi::pool() after calling analyse_mi_data().

analysis_obj

An optional analysis object (output of analyse_mi_data()), used to compute MI diagnostic statistics. When provided and the pooling method is Rubin's rules, the ARD includes additional stat rows for FMI, lambda, RIV, Barnard-Rubin adjusted df, complete-data df, relative efficiency, and the number of imputations per parameter. When NULL (the default), only the base ARD is returned.

conf.level

Confidence level used for CI labels (e.g., 0.95 produces "95% CI Lower"). If NULL (the default), the value is taken from pool_obj$conf.level. If that is also NULL, defaults to 0.95.

Value

A data frame of class "card" (ARD format) with grouping columns for visit (group1), parameter_type (group2), and lsm_type (group3). Each parameter produces rows for five statistics: estimate, std.error, conf.low, conf.high, and p.value, plus a method row. When analysis_obj is provided and the pooling method is Rubin's rules, additional diagnostic stat rows are included: fmi, lambda, riv, df.adjusted, df.complete, re, and m.imputations.

Details

The function works by:

1. Tidying the pool object via tidy_pool_obj()

2. Reshaping each parameter into long-format ARD rows (one row per statistic)

3. Adding grouping columns (visit, parameter_type, lsm_type)

4. Optionally enriching with MI diagnostic statistics when analysis_obj is provided

5. Applying cards::as_card() and cards::tidy_ard_column_order() for standard ARD structure

When analysis_obj is provided:

• For Rubin's rules pooling: diagnostic statistics (FMI, lambda, RIV, Barnard-Rubin adjusted df, relative efficiency) are computed per parameter using the per-imputation estimates, standard errors, and degrees of freedom.

• For non-Rubin pooling methods: an informative message is emitted and the base ARD is returned without diagnostic rows.

The resulting ARD passes cards::check_ard_structure() validation and is suitable for downstream use with gtsummary.

See also

• rbmi::pool() for creating pool objects

• analyse_mi_data() for creating analysis objects

• tidy_pool_obj() for the underlying data transformation

• cards::as_card() and cards::check_ard_structure() for ARD validation

Examples

# \donttest{
if (requireNamespace("cards", quietly = TRUE)) {
  library(rbmi)
  data("ADMI", package = "rbmiUtils")
  ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
  ADMI$USUBJID <- factor(ADMI$USUBJID)
  ADMI$AVISIT <- factor(ADMI$AVISIT)

  vars <- set_vars(
    subjid = "USUBJID", visit = "AVISIT", group = "TRT",
    outcome = "CHG", covariates = c("BASE", "STRATA", "REGION")
  )
  method <- method_bayes(
    n_samples = 20,
    control = control_bayes(warmup = 20, thin = 1)
  )

  ana_obj <- analyse_mi_data(ADMI, vars, method, fun = ancova)
  pool_obj <- pool(ana_obj)

  # Base ARD
  ard <- pool_to_ard(pool_obj)

  # Enriched ARD with MI diagnostics (FMI, lambda, RIV, df)
  ard_diag <- pool_to_ard(pool_obj, analysis_obj = ana_obj)
}
#> Warning: Data contains 100 imputations but method expects 20. Using first 20
#> imputations.
# }