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Lessons Learned from a Cross-validation between a Discrete-event Simulation Model and a Markov Model for Personalized Breast Cancer Treatment
| Content Provider | Semantic Scholar |
|---|---|
| Copyright Year | 2012 |
| Abstract | MO3 LESSONS LEARNED FROM A CROSS-VALIDATION BETWEEN A DISCRETE-EVENT SIMULATION MODEL AND A MARKOV MODEL FOR PERSONALIZED BREAST CANCER TREATMENT Jahn B1, Rochau U1, Arvandi M1, Kurzthaler C2 Saverno KR3 Fühne F2, Kluibenschaedl M4, Krahn M5, Paulden M6, Siebert U7 1UMIT; Oncotyrol Center for Personalized Cancer Medicine, Hall i.T.;Innsbruck, Tyrol, Austria, 2Oncotyrol Center for Personalized Cancer Medicine, Hall i.T.;Innsbruck, Tyrol, Austria, 3UMITUniv. for Health Sciences, Medical Informatics and Technology, Hall i.T., Austria; Univ. of Utah, Salt Lake City, USA, Hall i.T.;Innsbruck, Tyrol, Austria, 4Department of Public Health and Health Technology Assessment, UMIT University for Health Sciences, Hall i.T.;Innsbruck, Tyrol, Austria, 5Toronto Health Economics and Technology Assessment (THETA) Collaborative, Toronto, ON, Canada, 6University of Toronto, Toronto, ON, Canada, 7UMIT/ Oncotyrol/ Harvard University, Hall i.T.;Innsbruck, Tyrol, Austria OBJECTIVES: Breast cancer is the most common malignant disease in Western women. In the ONCOTYROL research center, a Breast Cancer Outcomes & Policy (ONCOTYROL) 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 has been validated. Major focus was on our experiences of the cross validation, i.e. the comparison of modeling results between the discrete-eventsimulation ONCOTYROL-model and the THETA-model (Toronto Health Economics and Technology Assessment Collaborative) which is a Markov model. Therefore, the Oncotyrol-model has been populated with the Canadian THETAmodel parameters. Cross validation started with a comparison of the natural history followed by QALYs and costs. RESULTS: The relative differences varied among the model outcomes. The smallest differences we found for costs, the highest for QALYs. All differences were smaller than 2.5%. The 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, definitions for meaningful differences and comparison techniques (mean estimates, distributions, multivariate outcomes). CONCLUSIONS: Cross-model validation was crucial to identify and correct modeling errors and to explain remaining differences of modeling results. However, small differences can lead to relevant changes in cost-effectiveness results. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://core.ac.uk/download/pdf/82135835.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
