Missing Data and Multiple Imputation

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Abstract

Missing data can result in biased estimates of the association between an exposure X and an outcome Y. Even in the absence of bias, missing data can hurt precision, resulting in wider confidence intervals. Analysts should examine the missing data pattern and try to determine the causes of the missingness. Modern software has simplified multiple imputation of missing data and the analysis of multiply imputed data to the point where this method should be part of any analyst's toolkit. Multiple imputation will often, but not always, reduce bias and increase precision compared with complete-case analysis. Some exceptions to this rule are noted in this review. When describing study results, authors should disclose the amount of missing data and other details. Investigators should consider how to minimize missing data when planning a study.

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