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Tutorial in Biostatistics

Multiple imputation using chained equations: Issues and guidance for practice

  1. Ian R. White1,*,
  2. Patrick Royston2,
  3. Angela M. Wood3

Article first published online: 30 NOV 2010

DOI: 10.1002/sim.4067

Issue

Statistics in Medicine

Statistics in Medicine

Volume 30, Issue 4, pages 377–399, 20 February 2011

Additional Information

How to Cite

White, I. R., Royston, P. and Wood, A. M. (2011), Multiple imputation using chained equations: Issues and guidance for practice. Statist. Med., 30: 377–399. doi: 10.1002/sim.4067

Author Information

  1. 1

    MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.

  2. 2

    Hub for Trials Methodology Research, MRC Clinical Trials Unit and University College London, 222 Euston Road, London NW1 2DA, U.K.

  3. 3

    Department of Public Health and Primary Care, University of Cambridge, Strangeways Research Laboratory, Worts Causeway, Cambridge CB2 8RN, U.K.

*MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, U.K.

Publication History

  1. Issue published online: 10 JAN 2011
  2. Article first published online: 30 NOV 2010
  3. Manuscript Accepted: 14 JUL 2010
  4. Manuscript Received: 3 SEP 2009

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Keywords:

  • missing data;
  • multiple imputation;
  • fully conditional specification

Abstract

Multiple imputation by chained equations is a flexible and practical approach to handling missing data. We describe the principles of the method and show how to impute categorical and quantitative variables, including skewed variables. We give guidance on how to specify the imputation model and how many imputations are needed. We describe the practical analysis of multiply imputed data, including model building and model checking. We stress the limitations of the method and discuss the possible pitfalls. We illustrate the ideas using a data set in mental health, giving Stata code fragments. Copyright © 2010 John Wiley & Sons, Ltd.

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