Multiple Imputation for Incomplete Data in Epidemiologic Studies

Abstract

Epidemiologic studies are frequently susceptible to missing information. Omitting observations with missing variables remains a common strategy in epidemiologic studies, yet this simple approach can often severely bias parameter estimates of interest if the values are not missing completely at random. Even when missingness is completely random, complete case analysis can reduce efficiency of estimated parameters, because large amounts of available data are simply tossed out with the incomplete observations. Alternative methods to mitigate missing information, such as multiple imputation, are becoming an increasing popular strategy to retain all available information, reduce potential bias, and improve efficiency in parameter estimation. In this paper, we describe the theoretical underpinnings of multiple imputation, and we illustrate application of this method in a collaborative challenge to assess the performance of various techniques to dealing with missing data. We detail the steps necessary to perform multiple imputation on a subset of the Collaborative Perinatal Project, where the goal is to estimate the odds of spontaneous abortion associated with smoking during pregnancy.