Tidy and Annotate a Pooled Object for Publication

This function processes a pooled analysis object of class pool into a tidy tibble format. It adds contextual information, such as whether a parameter is a treatment comparison or a least squares mean, dynamically identifies visit names from the parameter column, and provides additional columns for parameter type, least squares mean type, and visit.

tidy_pool_obj(pool_obj)

Arguments

pool_obj

A pooled analysis object of class pool.

Value

A tibble containing the processed pooled analysis results. The tibble includes columns for the parameter, description, estimates, standard errors, confidence intervals, p-values, visit, parameter type, and least squares mean type.

Details

The function rounds numeric columns to three decimal places for presentation. It dynamically processes the parameter column by separating it into components (e.g., type of estimate, reference vs. alternative arm, and visit), and provides informative descriptions in the output.

Examples

# Example usage:
library(dplyr)
library(rbmi)

data("ADMI")
N_IMPUTATIONS <- 100
BURN_IN <- 200
BURN_BETWEEN <- 5

# Convert key columns to factors
ADMI$TRT <- factor(ADMI$TRT, levels = c("Placebo", "Drug A"))
ADMI$USUBJID <- factor(ADMI$USUBJID)
ADMI$AVISIT <- factor(ADMI$AVISIT)

# Define key variables for ANCOVA analysis
 vars <- set_vars(
  subjid = "USUBJID",
  visit = "AVISIT",
  group = "TRT",
  outcome = "CHG",
  covariates = c("BASE", "STRATA", "REGION")  # Covariates for adjustment
 )

# Specify the imputation method (Bayesian) - need for pool step
method <- rbmi::method_bayes(
  n_samples = N_IMPUTATIONS,
  burn_in = BURN_IN,
  burn_between = BURN_BETWEEN
)

# Perform ANCOVA Analysis on Each Imputed Dataset
ana_obj_ancova <- analyse_mi_data(
  data = ADMI,
  vars = vars,
  method = method,
  fun = ancova,  # Apply ANCOVA
  delta = NULL   # No sensitivity analysis adjustment
)

pool_obj_ancova <- pool(ana_obj_ancova)
tidy_df <- tidy_pool_obj(pool_obj_ancova)

# Print tidy data frames
print(tidy_df)
#> # 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       -2.17   0.182 -2.53 
#> 2 lsm_ref_Week 24 Least Squa… Week… lsm            ref       0.0782 0.131 -0.179
#> 3 lsm_alt_Week 24 Least Squa… Week… lsm            alt      -2.09   0.126 -2.34 
#> 4 trt_Week 48     Treatment … Week… trt            NA       -3.81   0.256 -4.31 
#> 5 lsm_ref_Week 48 Least Squa… Week… lsm            ref       0.0481 0.185 -0.316
#> 6 lsm_alt_Week 48 Least Squa… Week… lsm            alt      -3.76   0.176 -4.11 
#> # ℹ 2 more variables: uci <dbl>, pval <dbl>