5N-3
THE ONCOTYROL PROSTATE CANCER OUTCOME AND POLICY MODEL - LESSONS LEARNED FROM NATURAL HISTORY CALIBRATION
Wednesday, October 22, 2014: 10:30 AM
Nikolai Mühlberger, DVM, MPH1, Eveline A.M. Heijnsdijk, PhD2, Christina Kurzthaler, MSc1, Murray D. Krahn, MD, MSc3, Willi Oberaigner, Priv.-Doz., Dr.4, Helmut Klocker, Univ.Prof., Mag.5, Annette Conrads-Frank, PhD6, Gaby Sroczynski, MPH, Dr.PH1 and Uwe Siebert, Prof., MD, MPH, MSc, ScD7, (1)Institute of Public Health, Medical Decision Making and HTA, UMIT - University for Health Sciences, Medical Informatics and Technology; Division of Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Hall i.T./Innsbruck, Austria, (2)Erasmus MC, Rotterdam, Netherlands, (3)Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, Toronto, ON, Canada, (4)Cancer Registry of Tyrol, TILAK GmbH, Innsbruck, Austria, (5)Research Laboratory of the Department of Urology, Innsbruck Medical University, Innsbruck, Austria, (6)Institute of Public Health, Medical Decision Making and HTA, UMIT - University for Health Sciences, Medical Informatics and Technology; Division of Health Technology Assessment and Bioinformatics, ONCOTYROL - Center for Personalized Cancer Medicine, Hall i.T/Innsbruck, Austria, (7)UMIT, Dept. Public Health&HTA/ ONCOTYROL, Area 4 HTA&Bioinformatics/ Harvard School Public Health, Center for Health Decision Science, Dept. Health Policy&Management/ Harvard Medical School, Institute for Technology Assessment&Dept. Radiology, Hall in Tyrol/ Innsbruck/ Boston, Austria
Purpose: The ONCOTYROL Prostate Cancer Outcome and Policy (PCOP) Model is a state-transition micro-simulation model designed to evaluate the benefits and harms of prostate cancer screening. Initially, the disease onset and progression module of the model was based on the structure and calibrated parameters of an early version of the Erasmus MISCAN model published in 2003. However, comparison with data from autopsy studies revealed an underestimation of the latent prevalence pool, which may cause an underestimation of overdiagnosis and bias the model favoring screening. Our objective was to recalibrate the model to latent prevalence data from autopsy studies, and to compare outputs of the recalibrated model to those of the previous version.
Method: Calibration of natural history parameters was carried out in two steps in collaboration with the authors of the Erasmus MISCAN model. First, we calibrated incidence of latent cancer to match age-specific prevalence data from autopsy studies. Second, we recalibrated all consecutive progression and detection parameters to match observed data of the Rotterdam cancer registry and the European trial (ERSPC). The calibration was performed using a deterministic state-transition model programmed in the statistical software R. As in the PCOP model, prostate cancer is separated into nine stage- and grade-specific health states. Transitions between cancer states were allowed to vary with dwelling time. Latent cancer can be detected by clinical symptoms or screening. Model parameters were fitted using the ‘nlminb’ optimization routine in R.
Result: In total, we calibrated 46 parameters. Deviations from observed data are modest and results robust against variations of the calibration approach. To compensate for the increase of the latent prevalence pool, additional parameters, allowing for an interruption of disease progression in the stage- and grade-specific health states had to be introduced to reach a good fit of the observed incidence data. Fitting of age-specific detection rates observed in the European trail required implementation of an effect modifier allowing for lower screening sensitivities in elder patients. Comparison of the recalibrated model with our previous model indicates that increasing the latent prevalence pool to match autopsy data result in considerable increase of overdiagnosis and lower screening sensitivity, which finally decreases the benefit-harm ratio of screening.
Conclusion: Calibration can help to better understand latent disease processes and to derive new hypotheses.
