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Analysis of longitudinal data of mixed types using a state space model approach

University of British Columbia

A new method for multivariate regression analysis of longitudinal data of mixed types is applied to the data from a sub-study of the Betaseron multicenter clinical trial in relapsing-remitting multiple sclerosis (MS) (The IFNB Multiple Sclerosis Study Group, 1993). The sub-study is based on a cohort of 52 patients at one center (University of British Columbia) for frequent magnetic resonance imagings (MRIs) for analysis of disease activity over the first two years of the trial (Paty, Li, the UBC MS/MRI Study Group and the IFNB Multiple Sclerosis Study Group, 1993). We consider a bivariate response vector with two different data types as components. The first component is a positive continuous variable and the second one is a count variable. We use a state space model approach based on the Tweedie class of exponential dispersion models assuming conditional independence of the two components given a latent gamma Markov process. The latent process is interpreted as the underlying severity of the disease whereas the observations reflect the symptoms. One advantage the new method offers is that it enables the examination of patterns over time. Not only can it identify the presence of treatment effect, but also the nature of the effect. It has well been established that Betaseron has substantially altered the natural history of MS in a properly controlled clinical trial (The IFNB Multiple Sclerosis Study Group, 1993). The main objective of this thesis is to illustrate the utilization of the new method using this data set and to extract additional valuable information from the data.

Thesis/Dissertation

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2009-03-24

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http://hdl.handle.net/2429/6389

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University of British Columbia

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