Estimating time to disease progression comparing transition models and survival methods--an analysis of multiple sclerosis data.

This article reports an analysis that aims to quantify the effect of fingolimod, an oral treatment for relapsing remitting multiple sclerosis (MS), on disability progression. The standard approach utilizes survival analysis methods, which may be problematic for MS studies that assess disability at only a few time points and include as a cardinal feature both relapses and remissions. Instead, a Markov transition model, originally developed in the framework of longitudinal data, is fit, and its special probabilistic properties are used to estimate survival curves for time to disability progression. The transition approach models the whole disability process and uses all available transition data for inference, while survival methods concentrate on a single event of interest and use only time to event data. This article compares the transition model approach to survival analysis methods, and discusses the differences in the interpretations of the estimated parameters. It applies both models to data obtained from two phase 3 clinical trials and finds that both yield positive effects for the new treatment compared to placebo, and provide similar estimates for the probability of disability progression over time. The transition model enables calculation of covariate-specific transition matrices that describe the short-term effect of treatment and other covariates on the disability process.