# Prepost Analysis with Pre-Imputed Datasets This function takes a list of already imputed datasets and runs prepost (resilience) regression analysis on each, then pools the results using Rubin's rules. Users are responsible for the imputation process, allowing maximum flexibility in imputation methods. ```r prepost_mi( data_list, formula, k = 1, nboot = 500, pool_method = "detailed", coef_labels = NULL, verbose = TRUE, ... ) ``` ## Arguments - `data_list`: List of imputed datasets (data frames) or a `mids` object from `mice` - `formula`: Formula for the prepost model (e.g., post_score ~ pre_score + age + bmi) - `k`: Resilience parameter (default = 1.2) - `nboot`: Number of bootstrap samples (default = 200) - `pool_method`: Pooling method: "detailed" (Barnard & Rubin) or "simple" (default = "detailed") - `coef_labels`: Optional named vector for renaming coefficients (e.g., c("age" = "Age (years)")) - `verbose`: Whether to print progress messages (default = TRUE) - `...`: Additional arguments passed to prepost() ## Returns A list containing: - **`pooled_results`**: The pooled estimates - **`results_table`**: Formatted results table - **`individual_results`**: List of results from each imputation - **`model_info`**: Information about the model specification - **`imputation_info`**: Summary of imputation characteristics ## Examples ```r ## Not run: # Example 1: List of data frames from any imputation method library(mice) data <- data.frame( post_score = rnorm(100), pre_score = rnorm(100), age = rnorm(100, 50, 10), bmi = rnorm(100, 25, 5) ) # Create some missing data data$age[1:10] <- NA data$bmi[5:15] <- NA # User does their own imputation (could be from any package/method) # Method 1: Using mice imp_mice <- mice(data, m = 5, printFlag = FALSE) imp_list <- list() for (i in 1:5) { imp_list[[i]] <- complete(imp_mice, i) } # Method 2: Using Amelia if (requireNamespace("Amelia", quietly = TRUE)) { imp_amelia <- Amelia::amelia(data, m = 5) imp_list <- imp_amelia$imputations } # Method 3: Using aregImpute from Hmisc if (requireNamespace("Hmisc", quietly = TRUE)) { set.seed(123) imp_areg <- Hmisc::aregImpute(~ post_score + pre_score + age + bmi, data = data, n.impute = 5) imp_list <- list() for (i in 1:5) { imp_data <- data imp_data$age[is.na(imp_data$age)] <- imp_areg$imputed$age[, i] imp_data$bmi[is.na(imp_data$bmi)] <- imp_areg$imputed$bmi[, i] imp_list[[i]] <- imp_data } } # Run resilience analysis on the pre-imputed list result <- prepost_mi( data_list = imp_list, formula = post_score ~ pre_score + age + bmi, k = 1.2, nboot = 200 ) # Example 2: Directly using a mice mids object result2 <- prepost_mi( data_list = imp_mice, # mids object formula = post_score ~ pre_score + age + bmi ) ## End(Not run) ``` ## See Also `prepost` for complete-case analysis without missing data `mice` for multiple imputation

prepost_mi function