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