Dr Pulak Ghosh, Georgia State University, USA

Joint Modelling of Multivariate Longitudinal Data for Mixed Responses with Application to Multiple Sclerosis Data

Multiple sclerosis (MS) is one of the most chronic neurological diseases in young adults with around 2.5 million affected individuals worldwide (Compston 2006).  The most common presenting symptoms are inflammation of the optic nerve, weakness, sensory disturbances, gait disturbances and bladder dysfunction.  So far only standard analysis methodology to estimate risks for relapse occurence has been used.  This includes mostly single endpoint survival analysis in which MRI information is shrunken to baseline values or aggregated measures such as means.  In the present analysis we aim to establish a model that llows the description and prediction of occurence of relapses by considering processes in the brain (visualized on T1 and T2 weighted MRI) simultaneously.  These complex processes, together with clinical baseline infromation, have never been considered in one model so far.  We will use our model to evaluate strength of dependencies of multivariate longitudinal MRI measures with the occurrence of MS relapses.