A New Framework for Managing and Analyzing Multiply Imputed Data in Stata

TLDR

The authors introduce a new Stata framework for analyzing multiply imputed data, aiming to streamline the handling of multiple imputed datasets. The framework stores all imputed datasets in a single vertically stacked file and offers a prefix command “mim” that supports any imputation method under simple rules, limits post‑estimation to valid options, and allows user overrides. Stacking the datasets simplifies management, and the mim command yields Rubin‑consistent parameter estimates for most Stata regressions, as illustrated with two published examples.

Abstract

A new set of tools is described for performing analyses of an ensemble of datasets that includes multiple copies of the original data with imputations of missing values, as required for the method of multiple imputation. The tools replace those originally developed by the authors. They are based on a simple data management paradigm in which the imputed datasets are all stored along with the original data in a single dataset with a vertically stacked format, as proposed by Royston in his ice and micombine commands. Stacking into a single dataset simplifies the management of the imputed datasets compared with storing them individually. Analysis and manipulation of the stacked datasets is performed with a new prefix command, mim, which can accommodate data imputed by any method as long as a few simple rules are followed in creating the imputed data. mim can validly fit most of the regression models available in Stata to multiply imputed datasets, giving parameter estimates and confidence intervals computed according to Rubin's results for multiple imputation inference. Particular attention is paid to limiting the available postestimation commands to those that are known to be valid within the multiple imputation context. However, the user has flexibility to override these defaults. Features of these new tools are illustrated using two previously published examples.