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COS3-1 EVALUATION OF PERSONALIZED BREAST CANCER TEST-TREATMENT STRATEGIES USING THE 21-GENE ASSAY RECURRENCE SCORE AND ADJUVANT! ONLINE: APPLICATION OF A DISCRETE EVENT SIMULATION MODEL

Tuesday, January 7, 2014: 1:30 PM
Tanglin IV (The Regent Hotel)

Beate Jahn, PhD1, Ursula Rochau, MD, MSc1, Christina Kurzthaler, Bsc1, Marjan Arvandi, MS1, Felicitas Kuehne1, Martina Kluibenschaedl, Bsc.1, Murray D. Krahn, MD, MSc2, Mike Paulden, MA., MSc.3 and Uwe Siebert, MD, MPH, MSc, ScD4, (1)UMIT - Institute of Public Health, Medical Decision Making and HTA/ Area 4 HTA and Bioinformatics, Hall in Tyrol/ Innsbruck, Austria, (2)University of Toronto, Toronto Health Economics and Technology Assessment (THETA) Collaborative, Toronto, ON, Canada, (3)University of Toronto, Toronto, ON, Canada, (4)UMIT/ ONCOTYROL/ Harvard School of Public Health/ Harvard Medical School, Hall, Austria
Purpose: A Breast Cancer Outcomes model was developed at the ONCOTYROL research center, to evaluate the cost-effectiveness of personalized test-treatment strategies in Austria. The goal of this study was to build a model that allows evaluation of combinations of innovative tests, biomarkers and new treatments early in the development process. In a first application, we evaluate the cost effectiveness of the new 21-gene assay (ODX) when it is applied in addition to the Adjuvant! Online (AOL) decision aid to support personalized decisions on adjuvant chemotherapy in Austria

Method: We developed a discrete event simulation model to run a hypothetical cohort of 50 year old women over a lifetime time horizon. The main outcomes were life-years gained, quality-adjusted life-years (QALYs), costs and cost-effectiveness. Based on the new ISPOR-SMDM recommendations, the model was validated. Eight test-treatment strategies were evaluated. Each strategy was defined by three letters. The first letter indicates whether patients with a low risk according to AOL were tested using ODX (Y-yes; N-no), the second and the third letters provide this information for AOL intermediate and high-risk patients, respectively. Robustness of the results was tested in a sensitivity analysis. Results were compared to a Canadian analysis by the Toronto Health Economics and Technology Assessment Collaborative (THETA).

Result:  Five out of eight strategies were dominated (i.e. more costly and less effective: NNY, NYN, YNN, YNY, YYN). The base-case analysis shows that only the strategies in which ODX is provided to patients with an intermediate or high AOL risk (incremental cost-effectiveness ratio (ICER) NYY = 1,600 EUR/QALY) and where all patients get ODX (ICER YYY 15,700 EUR/QALY) are cost-effective. These results are sensitive to changes in the probabilities of distant recurrence, age and costs of chemo that lead to further non dominated strategies. The base case analysis was comparable to the THETA results. Our discrete event simulation using a modular structure provides the flexibility to test various sequential tests, additional biomarker and treatments.

Conclusion: Our study showed that ODX, when used in addition to the AOL, is cost-effective in two test-treatment strategies (NYY, YYY) in Austria. Our simulation tool provides the flexibility to evaluate combinations of two or more tests that can complement each other and respective treatment.

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