Artikel
The application of cross-model validation to reduce uncertainty - Experiences from a personalized breast cancer model
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Autoren
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Beate Jahn
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria
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Ursula Rochau
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria
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Christina Kurzthaler
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria
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Marjan Arvandi
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria
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Kim Saverno
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Department of Pharmacotherapy, University of Utah, Salt Lake City, Utah, USA
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Felicitas Kühne
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria
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Martina Kluibenschädl
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria
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Murray Krahn
- Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, ON, Canada
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Mike Paulden
- Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto, ON, Canada
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Uwe Siebert
- Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, UMIT - University for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Division of Public Health Decision Modelling, Health Technology Assessment and Health Economics, ONCOTYROL - Center for Personalized Cancer Medicine, Innsbruck, Austria; Harvard School of Public Health, Boston, USA/ Harvard Medical School, Boston, USA
| Veröffentlicht: | 11. März 2013 |
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Gliederung
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Objectives: Decision analytic modeling allows evidence for short and long term benefits, harms and risks to be synthesized and therefore, can be used to support reimbursement decisions. Validation is an important step in the modeling process to quantify and reduce uncertainty and hereby building confidence in the model and results. At the ONCOTYROL Center for Personalized Cancer Medicine, a Breast Cancer Outcomes & Policy model was developed to evaluate the cost-effectiveness of the new 21-gene assay that supports personalized decisions on adjuvant chemotherapy. The goal of this study was to validate our ONCOTYROL-model.
Methods: The 21-gene assay was evaluated by simulating a hypothetical cohort of 50 year old women over a lifetime time horizon using a discrete event simulation. Main model outcomes were life-years gained, quality-adjusted life years (QALYs) and costs. Based on the new ISPOR-SMDM best practice recommendations, the model was validated. Cross validation between our model and a Markov model developed by THETA (Toronto Health Economics and Technology Assessment Collaborative) was our primary focus. Therefore, the ONCOTYROL model was populated with the Canadian THETA model parameters. Cross validation started with a comparison of the natural history followed by QALYs and costs.
Results: The relative differences between the results of the two models varied among the model outcomes, however all differences were smaller than 1.2%. The smallest differences were for costs and the highest were for QALYs. A comparison of the efficiency frontiers showed that small differences due to the modeling approach can lead to a different set of non-dominated test-treatment strategies.
The cross model validation involved several challenges: distinguishing between outcomes differences due to different modeling techniques and errors, determining what constitutes a meaningful difference, and utilizing various comparison techniques (mean estimates, distributions, multivariate outcomes).
Conclusions: Cross-model validation was crucial for identifying and correcting modeling errors and explaining differences in between modeling results. Small differences between models can change cost-effectiveness results.
This work was supported by the COMET Center ONCOTYROL, which is funded by the Austrian Federal Ministries BMVIT/BMWFJ (via FFG) and the Tiroler Zukunftsstiftung/Standortagentur Tirol (SAT).
