This paper outlines a strategy to validate multiple imputation methods. Rubin's criteria for proper multiple imputation are the point of departure. We describe a simulation method that yields insight into various aspects of bias and efficiency of the imputation process. We propose a new method for creating incomplete data under a general Missing At Random (MAR) mechanism. Software implementing the validation strategy is available as a SAS/IML module. The method is applied to investigate the behavior of polytomous regression imputation for categorical data.

Missing data mechanism, Multiple imputation, Proper imputation, Simulation
dx.doi.org/10.1111/1467-9574.00219, hdl.handle.net/1765/64949
Statistica Neerlandica
Department of Medical Informatics

Brand, J.P.L, van Buuren, S, Groothuis-Oudshoorn, K, & Gelsema, E.S. (2003). A toolkit in SAS for the evaluation of multiple imputation methods. Statistica Neerlandica (Vol. 57, pp. 36–45). doi:10.1111/1467-9574.00219