Assessment of Jointly Dependent Markov Processess Through Bayes Factors and Bayesian Variable Selection

• Activity Number: 541
• Type: Contributed
• Date/Time: Wednesday, August 7, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): David Engler*+ and Brian Healy
• Companies: Department of Statistics, Brigham Young University and Department of Neurology, Harvard Medical School
• Keywords: Bayesian variable selection ; Multiple Sclerosis ; Transition models ; Model selection

Abstract

In multiple sclerosis (MS), a primary clinical outcome measure is an ordinal score, the expanded disability severity score (EDSS). In relapsing-remitting MS, measures of relapse are additionally of interest. MS patients are typically assessed with regard to both the EDSS and relapse state at each follow up. As both are discrete measures, the two can be viewed as jointly dependent Markov processes. One of the main goals of MS research is to accurately model, over time, both transitions between EDSS states and change in relapse state. This objective first requires a number of significant modeling decisions, including decisions about whether or not the combination of specific disease states is warranted and assessment of the dependence structure between the two disease processes. Historically, such decisions are often made in an ad hoc manner and are not formally justified. We propose joint use of Bayes factors and Bayesian variable selection in the assessment of jointly dependent Markovian processes in MS. Methods are assessed using both simulated data and data collected from the Partners MS Center in Boston, MA.