Application of hidden Markov models to multiple sclerosis lesion count data.
R. Altman, and A. Petkau. Statistics in medicine, 24 (15):
2335-44(August 2005)3838<m:linebreak></m:linebreak>Anàlisi de dades; Count data.
DOI: 10.1002/sim.2108
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.
%0 Journal Article
%1 Altman2005b
%A Altman, Rachel MacKay
%A Petkau, A John
%D 2005
%J Statistics in medicine
%K Biological Humans MagneticResonanceImaging MarkovChains Models MultipleSclerosis Relapsing-Remitting Relapsing-Remitting:diagnosis Relapsing-Remitting:pathology
%N 15
%P 2335-44
%R 10.1002/sim.2108
%T Application of hidden Markov models to multiple sclerosis lesion count data.
%U http://www.ncbi.nlm.nih.gov/pubmed/15909288
%V 24
%X 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.
@article{Altman2005b,
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.},
added-at = {2023-02-03T11:44:35.000+0100},
author = {Altman, Rachel MacKay and Petkau, A John},
biburl = {https://www.bibsonomy.org/bibtex/262b3d2150823ec62a03c48c258165bc0/jepcastel},
doi = {10.1002/sim.2108},
interhash = {6caa88fbd508ef4d04641378be53d740},
intrahash = {62b3d2150823ec62a03c48c258165bc0},
issn = {0277-6715},
journal = {Statistics in medicine},
keywords = {Biological Humans MagneticResonanceImaging MarkovChains Models MultipleSclerosis Relapsing-Remitting Relapsing-Remitting:diagnosis Relapsing-Remitting:pathology},
month = {8},
note = {3838<m:linebreak></m:linebreak>Anàlisi de dades; Count data},
number = 15,
pages = {2335-44},
pmid = {15909288},
timestamp = {2023-02-03T11:44:35.000+0100},
title = {Application of hidden Markov models to multiple sclerosis lesion count data.},
url = {http://www.ncbi.nlm.nih.gov/pubmed/15909288},
volume = 24,
year = 2005
}