Journal
PSYCHOSOMATIC MEDICINE
Volume 68, Issue 3, Pages 427-436Publisher
LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/01.psy.0000221275.75056.d8
Keywords
missing data; full information maximum likelihood; direct maximum likelihood; maximum likelihood; multiple imputation; attrition
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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.
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