摘要：Modelling data from Multiple Sclerosis longitudinal studies
is a challenging topic since the phenotype of interest is typically ordinal;
time intervals between two consecutive measurements are nonconstant
and they can vary among individuals. Due to these unobservable
sources of heterogeneity statistical models for analysis of Multiple Sclerosis
severity evolve as a difficult feature. A few proposals have been
provided in the biostatistical literature (Heijtan (1991); Albert, (1994)) to
address the issue of investigating Multiple Sclerosis course. In this paper
Bayesian P-Splines (Brezger and Lang, (2006); Fahrmeir and Lang
(2001)) are indicated as an appropriate tool since they account for nonlinear
smooth effects of covariates on the change in Multiple Sclerosis
disability. By means of Bayesian P-Spline model we investigate both
the randomness affecting Multiple Sclerosis data as well as the ordinal
nature of the response variable.
