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Di Serio, C. and Lamina, C. (2003): Bayesian P-Splines to investigate the impact of covariates on Multiple Sclerosis clinical course. Collaborative Research Center 386, Discussion Paper 353 [PDF, 1MB]

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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.

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