Probabilistic models of the natural history of multiple sclerosis

Variability in the clinical course of multiple sclerosis is one of its most interesting and yet discouraging features. Research into patient and disease characteristics which might predict future disease course has produced conflicting results.This thesis presents an original approach in which the course of the disease is described by the movement of patients through well defined disease states. Two probabilistic models: the semi-Markov and the stochastic survival model are proposed to evaluate the effect of prognostic factors on transitions from state to state.The feasibility and applicability of both models is evaluated using data on the course of multiple sclerosis in 278 diagnosed patients from L'hopital Neurologique de Lyon who were followed from 1956-1976. The stochastic survival model is found to be the most appropriate. A short first remission, male sex, and multiple symptoms at onset are found to increase the risk of a poor prognosis