A Conditional Markov Model for Clustered Progressive Multistate Processes under Incomplete Observation
Version of Record online: 7 JUN 2004
Volume 60, Issue 2, pages 436–443, June 2004
How to Cite
Cook, R. J., Yi, G. Y., Lee, K.-A. and Gladman, D. D. (2004), A Conditional Markov Model for Clustered Progressive Multistate Processes under Incomplete Observation. Biometrics, 60: 436–443. doi: 10.1111/j.0006-341X.2004.00188.x
- Issue online: 7 JUN 2004
- Version of Record online: 7 JUN 2004
- Received February 2002. Revised October 2003. Accepted November 2003.
- Chronic disease;
- Markov process;
- Multistate process;
- Multivariate random effect;
- Panel data;
- Progressive model
Summary. Clustered progressive chronic disease processes arise when interest lies in modeling damage in paired organ systems (e.g., kidneys, eyes), in diseases manifest in different organ systems, or in systemic conditions for which damage may occur in several locations of the body. Multistate Markov models have considerable appeal for modeling damage in such settings, particularly when patients are only under intermittent observation. Generalizations are necessary, however, to deal with the fact that processes within subjects may not be independent. We describe a conditional Markov model in which the clustering in processes within subjects is addressed by the use of multiplicative random effects for each transition intensity. The random effects for the different transition intensities may be correlated within subjects, but are assumed to be independent for different subjects. We apply the mixed Markov model to a motivating data set of patients with psoriatic arthritis, and characterize the progressive course of damage in joints of the hand. A generalization to accommodate a subpopulation of “stayers” and extensions which facilitate regression are indicated and illustrated.