Routine multiple imputation in statistical databases

Abstract

This paper deals with problems concerning missing data in statistical databases. Multiple imputation is a statistically sound technique for handling incomplete data. Two problems should be addressed before the routine application of the technique becomes feasible. First, if imputations are to be appropriate for more than one statistical analysis, they should be generated independently of any scientific models that are to be applied to the data at a later stage. This is done by finding imputations that will extrapolate the structure of the data, as well as the uncertainty about this structure. A second problem is to use complete-data methods in an efficient way. The HERMES workstation encapsulates existing statistical packages in a client-server model. It forms a natural and convenient environment for implementing multiple imputation.