Multiple imputation of missing values: New features for mim

Patrick Royston (), John B. Carlin and Ian R. White
Additional contact information
Patrick Royston: MRC Clinical Trials Unit
John B. Carlin: Murdoch Children’s Research Institute and University of Melbourne
Ian R. White: MRC Biostatistics Unit

Stata Journal, 2009, vol. 9, issue 2, pages 252-264

Abstract: We present an update of mim, a program for managing multiply im- puted 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. Copyright 2009 by StataCorp LP.

Keywords: mim; multiple imputation; missing data; missing at random; ice; MICE (search for similar items in EconPapers)
Date: 2009

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Persistent link: http://EconPapers.repec.org/RePEc:tsj:stataj:v:9:y:2009:i:2:p:252-264

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