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Multiple imputation: review of theory, implementation and software
Author(s) -
Harel Ofer,
Zhou XiaoHua
Publication year - 2007
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2787
Subject(s) - imputation (statistics) , computer science , missing data , inference , software , data mining , data science , artificial intelligence , machine learning , programming language
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.