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

Jonathan W. Bartlett () and Tim P. Morris ()
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Jonathan W. Bartlett: London School of Hygiene and Tropical Medicine
Tim P. Morris: MRC Clinical Trials Unit at UCL

Stata Journal, 2015, vol. 15, issue 2, 437-456

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. Copyright 2015 by StataCorp LP.

Keywords: smcfcs; multiple imputation; substantive model compatible; congenial; interactions; nonlinearities (search for similar items in EconPapers)
Date: 2015
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