Application of hidden Markov models to multiple sclerosis lesion count data.
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.
Altman
Rachel MacKay
RM
Department of Statistics and Actuarial Science, Simon Fraser University, Canada. raltman@stst.sfu.ca
Petkau
A John
AJ
D006801:Humans; D008279:Magnetic Resonance Imaging; D008390:Markov Chains; D008954:Models, Biological; D020529:Multiple Sclerosis, Relapsing-Remitting / Q000175:diagnosis / Q000473:pathology*