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
pr@ctu.mrc.ac.uk
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John B. Carlin
Clinical Epidemiology and Biostatistics Unit
Murdoch
Children's Research Institute and University of Melbourne
Parkville,
Australia
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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.
View all articles by these authors:
Patrick Royston, John B. Carlin, Ian R. White
View all articles with these keywords:
mim, multiple imputation, missing data, missing at random, ice, MICE
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