title: The ONCOTYROL Prostate Cancer Outcome and Policy Model: Effect of Prevalence Assumptions on the Benefit-Harm Balance of Screening.
abstract: 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.
authors.affiliation: 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)
authors.forename: Nikolai
authors.initials: N
authors.lastname: Mühlberger
authors.affiliation: 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)
authors.forename: Christina
authors.initials: C
authors.lastname: Kurzthaler
authors.affiliation: 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)
authors.forename: Rowan
authors.initials: R
authors.lastname: Iskandar
authors.affiliation: 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)
authors.forename: Murray D
authors.initials: MD
authors.lastname: Krahn
authors.affiliation: Toronto General Research Institute, Toronto General Hospital, Toronto, Ontario, Canada (MDK, KEB)
authors.forename: Karen E
authors.initials: KE
authors.lastname: Bremner
authors.affiliation: Cancer Registry of Tyrol, TILAK GmbH, Innsbruck, Austria (WO)
authors.forename: Willi
authors.initials: W
authors.lastname: Oberaigner
authors.affiliation: Department of Urology, Innsbruck Medical University, Innsbruck, Austria (HK, WH)
authors.forename: Helmut
authors.initials: H
authors.lastname: Klocker
authors.affiliation: Department of Urology, Innsbruck Medical University, Innsbruck, Austria (HK, WH)
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Horninger
authors.affiliation: 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)
authors.forename: Annette
authors.initials: A
authors.lastname: Conrads-Frank
authors.affiliation: 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)
authors.forename: Gaby
authors.initials: G
authors.lastname: Sroczynski
authors.affiliation: 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)|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)
authors.forename: Uwe
authors.initials: U
authors.lastname: Siebert
mesh_terms: D015331:Cohort Studies; D003198:Computer Simulation; D003430:Cross-Sectional Studies; D003661:Decision Support Techniques; D018450:Disease Progression; D055088:Early Detection of Cancer / Q000706:statistics & numerical data*; D006801:Humans; D008297:Male; D008390:Markov Chains; D000069342:Medical Overuse / Q000706:statistics & numerical data; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011050:Policy Making; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000453:epidemiology* / Q000517:prevention & control; D018570:Risk Assessment / Q000706:statistics & numerical data*; D012984:Software
keywords: Markov models; cancer prevention; decision analysis; prostate cancer; simulation methods