Investigating determinants of multiple sclerosis in longitunal studies

A Bayesian approach

Clelia Di Serio, Claudia Lamina

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Article number198320
JournalJournal of Probability and Statistics
DOIs
Publication statusPublished - 2009

Fingerprint

Multiple Sclerosis
Bayesian Approach
Determinant
P-splines
Data Modeling
Longitudinal Study
Disability
Phenotype
Randomness
Statistical Model
Covariates
Consecutive
Vary
Interval

ASJC Scopus subject areas

  • Statistics and Probability

Cite this

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title = "Investigating determinants of multiple sclerosis in longitunal studies: A Bayesian approach",
abstract = "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.",
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