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Statistical Issues in Modeling Chronic Disease in Cohort Studies

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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.

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Acknowledgements

We thank the Editor and Jianwen Cai for inviting this article in connection with the Symposium on Emerging Methodological Issues in Population-Based Chronic Disease Research, in honor of Ross Prentice (October 20–21, 2011). This research was supported by grants from the Natural Sciences and Engineering Research Council of Canada and the Canadian Institutes for Health Research. Richard Cook is a Canada Research Chair in Statistical Methods for Health Research. The authors thank Dr. Dafna Gladman and Dr. Vinod Chandran for collaboration and helpful discussions regarding the research at the Centre for Prognosis Studies in Rheumatic Disease at the University of Toronto. The Diabetes Control and Complications Trial was sponsored by the Division of Diabetes, Endocrinology, and Metabolic Diseases of the National Institute of Diabetes and Digestive and Kidney Diseases, the National Institutes of Health, through cooperative agreements and a research grant. Additional support was provided by the National Heart, Lung, and Blood Institute, the National Eye Institute, and the National Center for Research Resources.

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  1. Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

    Richard J. Cook & Jerald F. Lawless

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Cook, R.J., Lawless, J.F. Statistical Issues in Modeling Chronic Disease in Cohort Studies. Stat Biosci 6, 127–161 (2014). https://doi.org/10.1007/s12561-013-9087-8

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