Multiple imputation as a missing data machine

Abstract

This paper deals with problems concerning missing data in clinical databases. After signalling some shortcomings of popular solutions to incomplete data problems, we outline the concepts behind multiple imputation. Multiple imputation is a statisticaly sound method for handling incomplete data. Application of multiple imputation requires a lot of work and not every user is able to do this. A transparent implementation of multiple imputation is necessary. Such an implementation is possible in the HERMES medical workstation. A remaining problem is to find proper imputations.