Modeling disease-state transition heterogeneity through Bayesian variable selection.

In many diseases, Markov transition models are useful in describing transitions between discrete disease states. Often the probability of transitioning from one state to another varies widely across subjects. This heterogeneity is driven, in part, by a possibly unknown number of previous disease states and by potentially complex relationships between clinical data and these states. We propose use of Bayesian variable selection in Markov transition models to allow estimation of subject-specific transition probabilities. Our approach simultaneously estimates the order of the Markov process and the transition-specific covariate effects. The methods are assessed using simulation studies and applied to model disease-state transition on the expanded disability status scale (EDSS) in multiple sclerosis (MS) patients from the Partners MS Center in Boston, MA. The proposed methodology is shown to accurately identify complex covariate-transition relationships in simulations and identifies a clinically significant interaction between relapse history and EDSS history in MS patients.