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The Stata Journal
Volume 15 Number 2: pp. 437-456

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Multiple imputation of covariates by substantive-model compatible fully conditional specification

Jonathan W. Bartlett
Department of Medical Statistics
London School of Hygiene and Tropical Medicine
London, UK
Tim P. Morris
MRC Clinical Trials Unit at UCL
Institute of Clinical Trials and Methodology
London School of Hygiene and Tropical Medicine
London, UK
Abstract.  Multiple imputation is a practical, principled approach to handling missing data. When used to impute missing values in covariates of regression models, imputation models may be misspecified if they are not compatible with the substantive model of interest for the outcome. In this article, we introduce the smcfcs command, which imputes covariates by substantive-model compatible fully conditional specification. This modifies the popular fully conditional specification or chained-equations approach to multiple imputation by imputing each covariate compatibly with a user-specified substantive model. We compare the smcfcs command with standard fully conditional specification imputation using mi impute chained in a simulation study and illustrative analysis of data from a study investigating time to tumor recurrence in breast cancer.
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