Multiple imputation: review of theory, implementation and software.

Full Abstract

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.(c) 2007 John Wiley & Sons, Ltd.

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Author information

Author/s: Harel, Ofer (O); Zhou, Xiao-Hua (XH);

Affiliation: Department of Statistics, University of Connecticut, 215 Glenbrook Road Unit 4120 Storrs, CT 06269-4120, USA. oharel(-atsign-)stat.uconn.edu

Grants: R01HL62567 (Agency:NHLBI NIH HHS) ; R01HS013105 (Agency:AHRQ HHS) ; U01 AG16976 (Agency:NIA NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, P.H.S.

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2007-Jul; vol 26 (issue 16) : pp 3057-77

Dates: Created 2007/06/14; Completed 2007/08/27; Revised 2007/12/03;

PMID: 17256804, status: MEDLINE (last retrieval date: 12/26/2008)

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

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MeSH headings (categories)

This article was linked to the MESH Headings shown below.

Bias (Epidemiology)
Diagnostic Tests, Routine - statistics & numerical data
Models, Statistical
Models, Theoretical

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