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The Stata Journal
Volume 8 Number 1: pp. 49-67



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A new framework for managing and analyzing multiply imputed data in Stata

John B. Carlin
Clinical Epidemiology & Biostatistics Unit
Murdoch Children's Research Institute &
University of Melbourne
Parkville, Australia
john.carlin@mcri.edu.au
John C. Galati
Clinical Epidemiology & Biostatistics Unit
Murdoch Children's Research Institute &
University of Melbourne
Parkville, Australia
Patrick Royston
Cancer and Statistical Methodology Groups
MRC Clinical Trials Unit
London, UK
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.
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