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Chapter 10: The University of Rochester Model of Breast Cancer Detection and Survival
Affiliations of authors: Department of Mathematics, Idaho State University, Pocatello, ID (LGH); Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY (LGH, AVZ, AYY); Department of Public Health Sciences, University of Toronto, Toronto, Ontario, Canada (AM)
Correspondence to: Andrei Y. Yakovlev, PhD, Department of Biosciences, University of Rochester, 601 Elamwood Ave., Box 630, Rochester, NY 14642 (e-mail: andrei_yakovlev{at}urmc.rochester.edu).
This paper presents a biologically motivated model of breast cancer development and detection allowing for arbitrary screening schedules and the effects of clinical covariates recorded at the time of diagnosis on posttreatment survival. Biologically meaningful parameters of the model are estimated by the method of maximum likelihood from the data on age and tumor size at detection that resulted from two randomized trials known as the Canadian National Breast Screening Studies. When properly calibrated, the model provides a good description of the U.S. national trends in breast cancer incidence and mortality. The model was validated by predicting some quantitative characteristics obtained from the Surveillance, Epidemiology, and End Results data. In particular, the model provides an excellent prediction of the size-specific age-adjusted incidence of invasive breast cancer as a function of calendar time for 1975–1999. Predictive properties of the model are also illustrated with an application to the dynamics of age-specific incidence and stage-specific age-adjusted incidence over 1975–1999.
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