Choosing Appropriate Methods for Missing Data in Medical Research: A Decision Algorithm on Methods for Missing Data

Hatice UENAL
Benjamin MAYER
Jean-Baptist DU PREL

Keywords

Missing Data, Decision Algorithm, Imputation Methods, Multiple Imputation

Abstract

Missing data (MD) are a common problem in medical research. When ignored or treated not appropriately, MD can lead to seriously biased results. Currently, there are no comprehensive guidelines for efficiently identifying suitable imputation methods in different MD situations. The objective of the paper is to discuss various methods to handle missing data. Based on a selective literature search, common MD imputation methods were identified. A decision algorithm is presented where the considered methods are prioritized with respect to the underlying missing data mechanism and scale level of the incomplete data. Furthermore, all included imputation methods are described in more detail. No alternative decision algorithms for MD imputation methods of this complexity have been developed yet, wherefore it could serve as a useful tool for researchers confronted with MD.