title: 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 statistically 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.
authors.affiliation: Dept. of Medical Informatics, Erasmus University Rotterdam, The Netherlands.
authors.forename: J
authors.initials: J
authors.lastname: Brand
authors.forename: S
authors.initials: S
authors.lastname: van Buuren
authors.forename: E M
authors.initials: EM
authors.lastname: van Mulligen
authors.forename: T
authors.initials: T
authors.lastname: Timmers
authors.forename: E
authors.initials: E
authors.lastname: Gelsema
mesh_terms: D000465:Algorithms; D003199:Computer Systems; D003627:Data Interpretation, Statistical; D003628:Database Management Systems; D008432:Mathematical Computing