Evaluating treatment effect within a multivariate stochastic ordering framework: Nonparametric combination methodology applied to a study on multiple sclerosis

Abstract

Multiple sclerosis is an autoimmune complex disease that affects the central nervous system. It has a multitude of symptoms that are observed in different people in many different ways. At this time, there is no definite cure for multiple sclerosis. However, therapies that slow the progression of disability, controlling symptoms and helping patients to maintain a normal quality of life, are available. We will focus on relapsing–remitting multiple sclerosis patients treated with interferons or glatiramer acetate. These treatments have been shown to be effective, but their relative effectiveness has not been well established yet. To assess the superiority of a treatment, instead of classical parametric methods, we propose a statistical approach within the permutation setting and the nonparametric combination of dependent permutation tests. In this framework, we may easily handle with hypothesis testing problems for multivariate monotonic stochastic ordering. This approach has been motivated by the analysis of a large observational Italian multicentre study on multiple sclerosis, with several continuous and categorical outcomes measured at multiple time points.