Statistical Issues in Modeling Chronic Disease in Cohort Studies
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Date
2014
Authors
Cook, Richard J.
Lawless, Jerald F.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer
Abstract
Observational cohort studies of individuals with chronic disease provide information on rates of
disease progression, the effect of fixed and time-varying risk factors, and the extent of heterogeneity
in the course of disease. Analysis of this information is often facilitated by the use of multistate
models with intensity functions governing transition between disease states. We discuss modeling
and analysis issues for such models when individuals are observed intermittently. Frameworks for
dealing with heterogeneity and measurement error are discussed including random effect models,
finite mixture models, and hidden Markov models. Cohorts are often defined by convenience and
ways of addressing outcome-dependent sampling or observation of individuals are also discussed.
Data on progression of joint damage in psoriatic arthritis and retinopathy in diabetes are analysed
to illustrate these issues and related methodology.
Description
The final publication (Cook, R. J., & Lawless, J. F. (2014). Statistical issues in modeling chronic disease in cohort studies. Statistics in Biosciences, 6(1), 127-161. DOI: 10.1007/s12561-013-9087-8) is available at Springer via http://link.springer.com/article/10.1007/s12561-013-9087-8
Keywords
heterogeneity, intermittent observation, Markov processes, multistate models, life history studies