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Journal of Probability and Statistics
Volume 2009, Article ID 198320, 24 pages
Research Article

Investigating Determinants of Multiple Sclerosis in Longitunal Studies: A Bayesian Approach

1University Centre of Statistics in the Biomedical Sciences, (CUSSB), Vita-Salute University, Milan, Italy
2Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria

Received 3 September 2008; Revised 3 February 2009; Accepted 12 August 2009

Academic Editor: Kelvin K. W. Yau

Copyright © 2009 Clelia Di Serio and Claudia Lamina. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.