resilience (version 2025.1.1)

prepost_mi: Prepost Analysis with Pre-Imputed Datasets

Description

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.

Usage

prepost_mi(
  data_list,
  formula,
  k = 1,
  nboot = 500,
  pool_method = "detailed",
  coef_labels = NULL,
  verbose = TRUE,
  ...
)

Value

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

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()

See Also

prepost for complete-case analysis without missing data mice for multiple imputation

Examples

if (FALSE) {
# 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
)
}