title: Application of hidden Markov models to multiple sclerosis lesion count data.
abstract: This paper is motivated by the work of Albert et al. who consider lesion count data observed on multiple sclerosis patients, and develop models for each patient's data individually. From a medical perspective, adequate models for such data are important both for describing the behaviour of lesions over time, and for designing efficient clinical trials. In this paper, we discuss some issues surrounding the hidden Markov model proposed by these authors. We describe an efficient estimation method and propose some extensions to the original model. Our examples illustrate the need for models which describe all patients' data simultaneously, while allowing for inter-patient heterogeneity.
authors.affiliation: Department of Statistics and Actuarial Science, Simon Fraser University, Canada. raltman@stst.sfu.ca
authors.forename: Rachel MacKay
authors.initials: RM
authors.lastname: Altman
authors.forename: A John
authors.initials: AJ
authors.lastname: Petkau
mesh_terms: D006801:Humans; D008279:Magnetic Resonance Imaging; D008390:Markov Chains; D008954:Models, Biological; D020529:Multiple Sclerosis, Relapsing-Remitting / Q000175:diagnosis / Q000473:pathology*