Models for two-state disease processes with applications to relapsing-remitting multiple sclerosis

In diseases like relapsing-remitting multiple sclerosis (MS), patients experience repeated transitions between symptom-free and symptomatic disease states (the symptomatic state is called an exacerbation). Analyses for this kind of data commonly ignore the information available on the second state (the lengths of the exacerbations, for example). In this thesis, we consider models that incorporate the second state into the analyses. The basic stochastic models are Markov chains, alternating renewal processes and marked point processes. For the Markov chains and alternating renewal process models, we consider simple fixed effects models as well as random effects models where the random effects are introduced to allow for heterogeneity between patients and correlation of data on one patient. For these models, the statistical inference is based on maximum likelihood. For the marked point process model, we use a generalized estimating equation approach. We apply these models to a data set from a MS clinical trial. The aim of the analyses is to relate the available covariates to the disease process. We do not attempt a comprehensive analysis of the data set, rather the aim here is to see what can be achieved and which questions can be addressed with the different models.Science, Faculty ofStatistics, Department ofGraduat