Multiple imputation by chained equations: what is it and how does it work?

被引:2342
作者
Azur, Melissa J. [1 ]
Stuart, Elizabeth A. [1 ]
Frangakis, Constantine [2 ]
Leaf, Philip J. [1 ]
机构
[1] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Mental Hlth, Baltimore, MD USA
[2] Johns Hopkins Bloomberg Sch Publ Hlth, Dept Biostat, Baltimore, MD USA
关键词
missing data; multiple imputation; analyze; MISSING-DATA; VALUES;
D O I
10.1002/mpr.329
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Multivariate imputation by chained equations (MICE) has emerged as a principled method of dealing with missing data. Despite properties that make MICE particularly useful for large imputation procedures and advances in software development that now make it accessible to many researchers, many psychiatric researchers have not been trained in these methods and few practical resources exist to guide researchers in the implementation of this technique. This paper provides an introduction to the MICE method with a focus on practical aspects and challenges in using this method. A brief review of software programs available to implement MICE and then analyze multiply imputed data is also provided. Copyright (C) 2011 John Wiley & Sons, Ltd.
引用
收藏
页码:40 / 49
页数:10
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