Multiple Imputation by Chained Equation

Abstract

In most observational and experimental studies, missing data certainly happens and adequate treatments are required to prevent bias and loss of efficiency of the statistical inference. However, the missing generally occurs in multiple variables with different patterns in individual subjects. Although valid statistical inference methods are needed in these situations, most existing methods require complicated statistical models and computations. The multiple imputation by chained equation (MICE) is an effective method that can be applied in these situations, and has been widely used in many observational and experimental studies in recent years. Also, many useful statistical packages have been developed for standard statistical software recently. In this article, we provide a gentle tutorial on the MICE methodology with concrete applications to an ovarian cancer clinical study (Clark and Altman, 2003; J. Clin. Epidemiol. 56, 28-37).