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The Stata Journal
Volume 9 Number 2: pp. 252-264

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Multiple imputation of missing values: New features for mim

Patrick Royston
Hub for Trials Methodology Research
MRC Clinical Trials Unit and University College London
London, UK
John B. Carlin
Clinical Epidemiology and Biostatistics Unit
Murdoch Children's Research Institute and University of Melbourne
Parkville, Australia
Ian R. White
MRC Biostatistics Unit
Institute of Public Health
Cambridge, UK
Abstract.  We present an update of mim, a program for managing multiply imputed datasets and performing inference (estimating parameters) using Rubin’s rules for combining estimates from imputed datasets. The new features of particular importance are an option for estimating the Monte Carlo error (due to the sampling variability of the imputation process) in parameter estimates and in related quantities, and a general routine for combining any scalar estimate across imputations.
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