Multiple imputation using chained equations: Issues and guidance for practice

被引：6476

作者：

White, Ian R. ^{[1
]}

Royston, Patrick ^{[2
,3
]}

Wood, Angela M. ^{[4
]}

机构：

[1] Inst Publ Hlth, MRC Biostat Unit, Cambridge CB2 0SR, England

[2] MRC Clin Trials Unit, London NW1 2DA, England

[3] UCL, London NW1 2DA, England

[4] Univ Cambridge, Dept Publ Hlth & Primary Care, Strangeways Res Lab, Cambridge CB2 8RN, England

来源：

STATISTICS IN MEDICINE | 2011年 / 30卷 / 04期

基金：

英国医学研究理事会;

关键词：

missing data; multiple imputation; fully conditional specification; MISSING VALUES; IMPUTED DATA; REGRESSION; INFERENCE; EMPHASIS; UPDATE; ICE;

D O I：

10.1002/sim.4067

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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 (C) 2010 John Wiley & Sons, Ltd.

引用

页码：377 / 399

页数：23

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