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Med Decis Making. 2015 Aug;35(6):758-72. doi: 10.1177/0272989X15585114. Epub 2015 May 14.

The ONCOTYROL Prostate Cancer Outcome and Policy Model: Effect of Prevalence Assumptions on the Benefit-Harm Balance of Screening.

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Department of Public Health and Health Technology Assessment, UMIT-University for Health Sciences, Medical Informatics and Technology, Tyrol, Austria (NM, CK, RI, ACF, GS, US)
Division of Health Technology Assessment and Bioinformatics, ONCOTYROL-Center for Personalized Cancer Medicine, Innsbruck, Austria (NM, CK, RI, ACF, GS, US)
Department of Health Policy and Management, University of Minnesota, Minneapolis, MN, USA (RI)
Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto, Ontario, Canada (MDK)
Toronto General Research Institute, Toronto General Hospital, Toronto, Ontario, Canada (MDK, KEB)
Cancer Registry of Tyrol, TILAK GmbH, Innsbruck, Austria (WO)
Department of Urology, Innsbruck Medical University, Innsbruck, Austria (HK, WH)
Center for Health Decision Science, Department of Health Policy and Management, Harvard School of Public Health, Boston, MA, USA (US)
Institute for Technology Assessment and Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA (US)



The ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) model is a state-transition microsimulation model evaluating the benefits and harms of prostate cancer (PCa) screening. The natural history and detection component of the original model was based on the 2003 version of the Erasmus MIcrosimulation SCreening ANalysis (MISCAN) model, which was not calibrated to prevalence data. Compared with data from autopsy studies, prevalence of latent PCa assumed by the original model is low, which may bias the model toward screening. Our objective was to recalibrate the original model to match prevalence data from autopsy studies as well and compare benefit-harm predictions of the 2 model versions differing in prevalence.


For recalibration, we reprogrammed the natural history and detection component of the PCOP model as a deterministic Markov state-transition cohort model in the statistical software package R. All parameters were implemented as variables or time-dependent functions and calibrated simultaneously in a single run. Observed data used as calibration targets included data from autopsy studies, cancer registries, and the European Randomized Study of Screening for Prostate Cancer. Compared models were identical except for calibrated parameters.


We calibrated 46 parameters. Prevalence from autopsy studies could not be fitted using the original parameter set. Additional parameters, allowing for interruption of disease progression and age-dependent screening sensitivities, were needed. Recalibration to higher prevalence demonstrated a considerable increase of overdiagnosis and decline of screening sensitivity, which significantly worsened the benefit-harm balance of screening.


Our calibration suggests that not all cancers are at risk of progression, and screening sensitivity may be lower at older ages. PCa screening models that use calibration to simulate disease progression in the unobservable latent phase are highly sensitive to prevalence assumptions.


Markov models; cancer prevention; decision analysis; prostate cancer; simulation methods

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