Bayesian P-Splines to investigate the impact of covariates on Multiple Sclerosis clinical course

This paper aims at proposing suitable statistical tools to address
heterogeneity in repeated measures, within a Multiple Sclerosis
(MS) longitudinal study. Indeed, due to unobservable sources of
heterogeneity, modelling the effect of covariates on MS severity
evolves as a very difficult feature. Bayesian P-Splines are
suggested for modelling non linear smooth effects of covariates
within generalized additive models. Thus, based on a pooled MS data
set, we show how extending Bayesian P-splines to mixed effects
models (Lang and Brezger, 2001), represents an attractive
statistical approach to investigate the role of prognostic factors
in affecting individual change in disability.