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Biostatistics 3:407-420 (2002)
© 2002 Oxford University Press

A generalized mover–stayer model for panel data

Richard J. Cook, John D. Kalbfleisch and Grace Y. Yi

Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, Ontario, Canada N2L 3G1 rjcook{at}vwaterloo.ca

A generalized mover–stayer model is described for conditionally Markov processes under panel observation. Marginally the model represents a mixture of nested continuous-time Markov processes in which sub-models are defined by constraining some transition intensities to zero between two or more states of a full model. A Fisher scoring algorithm is described which facilitates maximum likelihood estimation based only on the first derivatives of the transition probability matrices. The model is fit to data from a smoking prevention study and is shown to provide a significant improvement in fit over a time-homogeneous Markov model. Extensions are developed which facilitate examination of covariate effects on both the transition intensities and the mover–stayer probabilities.

Keywords: Latent variables; Marginal likelihood; Markov model; Multi-state process; Time homogeneous intensity


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