3G-3 WHEN IS ENOUGH EVIDENCE ENOUGH? VALUE-OF-INFORMATION ANALYSIS FOR PRIORITIZING ADDITIONAL OUTCOMES RESEARCH ON THE TREATMENT OF CHRONIC MYELOID LEUKEMIA

Tuesday, October 21, 2014: 11:00 AM

Ursula Rochau, MD, MSc1, Felicitas Kuehne, MSc2, Beate Jahn, PhD3, Christina Kurzthaler, MSc3, Albana Muka, none3, Isaac Corro Ramos, PhD4, Bjoern Stollenwerk, PD, PhD5, Jeremy Goldhaber-Fiebert, PhD6 and Uwe Siebert, Prof., MD, MPH, MSc, ScD7, (1)UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and HTA, Department of Public Health and HTA/ ONCOTYROL - Center for Personalized Cancer Medicine, Area 4 HTA and Bioinformatics, Hall in Tyrol/ Innsbruck, Austria, (2)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, (3)UMIT - University for Health Sciences, Medical Informatics and Technology, Institute of Public Health, Medical Decision Making and Health Technology Assessment, Department of Public Health and Health Technology Assessment, Hall in Tyrol, Austria, (4)Erasmus University Rotterdam, Institute for Medical Technology Assessment, Rotterdam, Netherlands, (5)Helmholtz Zentrum Muenchen (GmbH), German Research Center for Environmental Health, Institute of Health Economics and Health Care Management, Neuherberg, Germany, (6)Stanford University, Stanford Health Policy, Centers for Health Policy and Primary Care and Outcomes Research, Department of Medicine, Stanford, CA, (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: Value-of-Information (VoI) analysis is an important tool for the systematic assessment of the need for further research in the presence of decision uncertainty. Our aim is to guide decision regarding future outcomes research on parameters related to different regimens for chronic myeloid leukemia (CML) using VoI analysis.

Methods: We updated a previously developed state-transition Markov model of CML, which evaluates seven treatment regimens including different combinations of tyrosine kinase inhibitors, chemotherapy and stem cell transplantation (SCT). For model parameters, we used published trial data, and Austrian clinical, epidemiological, and economic data. We performed a cohort simulation over a lifetime horizon, adopted a societal perspective, and discounted costs and benefits at 3% annually. For the probabilistic sensitivity analysis and the VoI analysis, we defined parameter uncertainty distributions from our source data. We calculated the expected value of perfect information (EVPI), partial perfect information (EVPPI), and the population EVPI (PEVPI). Additionally, we examined the expected value of sample information (EVSI) for different trial sizes. The goal was to estimate the expected benefit of future research and identify parameters whose further study was most valuable for resolving decision uncertainty.

Results: Three strategies are on the efficiency frontier: imatinib-->chemotherapy/SCT, nilotinib-->chemotherapy/SCT (140,000 €/QALY) and nilotinib-->dasatinib-->chemotherapy/SCT (176,000 €/QALY). The EVPI for eliminating all uncertainty results in a curve with two peaks. One peak is around a WTP threshold of 150,000 €/QALY with an EVPI of 4,600 € and another peak can be found at 180,000 €/QALY with an EVPI of 7,700 € (Figure 1). The PEVPI for Austria assuming a 10-year technology horizon was 2.5 million € (WTP 150,000 €/QALY) and 4.5 million € (WTP 180,000 €/QALY). EVPPI identified four parameters most responsible for decision uncertainty: Duration of first-line TKI-therapy, probability of progressing from chronic phase to accelerated phase of disease, probability of receiving a SCT after therapy failure, and the utility after SCT of suffering from chronic graft-versus-host disease. EVSI commented on the optimal study size for these parameters given the cost of obtaining information.

Conclusions: Acquiring additional evidence could prove valuable for determining optimal treatment regimens for chronic myeloid leukemia. If further research were funded, studies should examine a combination of natural history, treatment, and quality of life parameters, especially the effectiveness of first-line TKI treatment.