A primer on the use of modern missing-data methods in psychosomatic medicine research
A primer on the use of modern missing-data methods in psychosomatic medicine research
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DOI：
10.1097/01.psy.0000221275.75056.d8
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发表时间：
2006-05-01
影响因子：
3.3
通讯作者：
Enders, Craig K.
中科院分区：
文献类型：
--
作者：
Enders, Craig K.
本文总结了与缺失数据相关的近期方法学进展，并概述了两种“现代”分析方法，即直接最大似然（DML）估计和多重填补（MI）。文章首先概述了由鲁宾阐述的缺失数据理论。对传统的缺失数据技术进行了简要描述，并更详细地阐述了DML和MI；特别关注了一种将辅助变量纳入分析模型的“包容性”分析策略。文章最后使用一个人造的生活质量数据集进行了示例分析。提供了所有DML和MI分析的计算机代码，并举例说明了辅助变量的纳入。
This paper summarizes recent methodologic advances related to missing data and provides an overview of two "modern" analytic options, direct maximum likelihood (DML) estimation and multiple imputation (MI). The paper begins with an overview of missing data theory, as explicated by Rubin. Brief descriptions of traditional missing data techniques are given, and DML and 141 are outlined in greater detail; special attention is given to an "inclusive" analytic strategy that incorporates auxiliary variables into the analytic model. The paper concludes with an illustrative analysis using an artificial quality of life data set. Computer code for all DML and MI analyses is provided, and the inclusion of auxiliary variables is illustrated.