Multiple Imputation for Longitudinal Data Under a Bayesian Multilevel Model

Hakan Demirtas

Communications in Statistics - Theory and Methods, 2009, vol. 38, issue 16-17, 2812-2828

Abstract: In this article, I establish a connection between Bayesian random-coefficient pattern-mixture models that were described by Demirtas (2005), and the idea of converting binary and ordinal longitudinal outcomes to multivariate normal outcomes in a sensible way so that re-conversion to the original scale yields the original specified marginal expectations and correlations after performing multiple imputation (Demirtas and Hedeker, 2007, 2008a). I also illustrate the use of these methods via a real data set from schizophrenia research.

Date: 2009
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DOI: 10.1080/03610920902947162

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