Multiple imputation: review of theory, implementation and software
Article first published online: 29 JAN 2007
Copyright © 2007 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 26, Issue 16, pages 3057–3077, 20 July 2007
How to Cite
Harel, O. and Zhou, X.-H. (2007), Multiple imputation: review of theory, implementation and software. Statist. Med., 26: 3057–3077. doi: 10.1002/sim.2787
- Issue published online: 6 JUN 2007
- Article first published online: 29 JAN 2007
- Manuscript Accepted: 6 NOV 2006
- Manuscript Received: 3 JUN 2005
- Unknown funding agency. Grant Numbers: R01HL62567, AHRQ R01HS013105, U01 AG16976
- multiple imputation;
- sensitivity and specificity;
- diagnostic tests
Missing data is a common complication in data analysis. In many medical settings missing data can cause difficulties in estimation, precision and inference. Multiple imputation (MI) (Multiple Imputation for Nonresponse in Surveys. Wiley: New York, 1987) is a simulation-based approach to deal with incomplete data. Although there are many different methods to deal with incomplete data, MI has become one of the leading methods. Since the late 1980s we observed a constant increase in the use and publication of MI-related research. This tutorial does not attempt to cover all the material concerning MI, but rather provides an overview and combines together the theory behind MI, the implementation of MI, and discusses increasing possibilities of the use of MI using commercial and free software. We illustrate some of the major points using an example from an Alzheimer disease (AD) study. In this AD study, while clinical data are available for all subjects, postmortem data are only available for the subset of those who died and underwent an autopsy. Analysis of incomplete data requires making unverifiable assumptions. These assumptions are discussed in detail in the text. Relevant S-Plus code is provided. Copyright © 2007 John Wiley & Sons, Ltd.