A CRITICAL-LOOK AT METHODS FOR HANDLING MISSING COVARIATES IN EPIDEMIOLOGIC REGRESSION-ANALYSES

Epidemiologic studies often encounter missing covariate values. While simple methods such as stratification on missing-data status, conditio nal-mean imputation, and complete-subject analysis are commonly employ ed for handling this problem, several studies have shown that these me thods can be biased under reasonable circumstances. The authors review these results in the context of logistic regression and present simul ation experiments showing the limitations of the methods, The method b ased on missing-data indicators can exhibit severe bias even when the data are missing completely at random, and regression (conditional-mea n) imputation can be inordinately sensitive to model misspecification. Even complete-subject analysis can outperform these methods. More sop histicated methods, such as maximum likelihood, multiple imputation, a nd weighted estimating equations, have been given extensive attention in the statistics literature, While these methods are superior to simp le methods, they are not commonly used in epidemiology, no doubt due t o their complexity and the lack of packaged software to apply these me thods. The authors contrast the results of multiple imputation to simp le methods in the analysis of a case-control study of endometrial canc er, and they find a meaningful difference in results for age at menarc he. In general, the authors recommend that epidemiologists avoid using the missing-indicator method and use more sophisticated methods whene ver a large proportion of data are missing.