OBJECTIVE: In a budget-constrained health-care system, decisions about investing in strategies to promote implementation have to be made alongside decisions about health-care provision and research funding. Using a Bayesian decision-theoretic approach, an analytic framework has been developed to inform these separate but related decisions, establishing the expected value of both perfect information (EVPI) and perfect implementation (EVPIM). We applied this framework to inform decision-making about resource allocation to metastatic hormone-refractory prostate cancer (mHRPC) in the UK. METHODS: Based on available evidence on the cost-effectiveness of all plausible treatments for mHRPC, we determined which treatment option(s) were cost-effective and explored the uncertainty surrounding this decision. Given the decision uncertainty and the variation in care provided by health-care professionals, we then determined the EVPI and EVPIM. Finally, we performed sensitivity analyses to explore the influence of alternative assumptions regarding various decision parameters on the efficiency of resource allocation. RESULTS: Depending on the cost-effectiveness threshold (lambda), we identified mitoxantrone plus prednisone/prednisolone and docetaxel plus prednisone/prednisolone (3 weekly) as the optimal treatments for mHRPC. Given current clinical practice, there appears to be considerable scope for improving the efficiency of health-care provision: the EVPI (estimated to be over pound13 million) indicates that acquiring further information could be cost-effective; and the EVPIM (estimated to be over pound4 million) suggests that investing in strategies to implement the treatments regimens being identified as optimal is potentially worthwhile. Through sensitivity analyses, we found that the EVPI and EVPIM are mainly driven by lambda, the number of treatment options being considered, the current level of implementation, and the size of the eligible patient population. CONCLUSION: The application demonstrates that the framework provides a simple and useful analytic tool for decision-makers to address resource allocation problems between health-care provision, further research, and implementation efforts.
OBJECTIVE: In a budget-constrained health-care system, decisions about investing in strategies to promote implementation have to be made alongside decisions about health-care provision and research funding. Using a Bayesian decision-theoretic approach, an analytic framework has been developed to inform these separate but related decisions, establishing the expected value of both perfect information (EVPI) and perfect implementation (EVPIM). We applied this framework to inform decision-making about resource allocation to metastatic hormone-refractory prostate cancer (mHRPC) in the UK. METHODS: Based on available evidence on the cost-effectiveness of all plausible treatments for mHRPC, we determined which treatment option(s) were cost-effective and explored the uncertainty surrounding this decision. Given the decision uncertainty and the variation in care provided by health-care professionals, we then determined the EVPI and EVPIM. Finally, we performed sensitivity analyses to explore the influence of alternative assumptions regarding various decision parameters on the efficiency of resource allocation. RESULTS: Depending on the cost-effectiveness threshold (lambda), we identified mitoxantrone plus prednisone/prednisolone and docetaxel plus prednisone/prednisolone (3 weekly) as the optimal treatments for mHRPC. Given current clinical practice, there appears to be considerable scope for improving the efficiency of health-care provision: the EVPI (estimated to be over pound13 million) indicates that acquiring further information could be cost-effective; and the EVPIM (estimated to be over pound4 million) suggests that investing in strategies to implement the treatments regimens being identified as optimal is potentially worthwhile. Through sensitivity analyses, we found that the EVPI and EVPIM are mainly driven by lambda, the number of treatment options being considered, the current level of implementation, and the size of the eligible patient population. CONCLUSION: The application demonstrates that the framework provides a simple and useful analytic tool for decision-makers to address resource allocation problems between health-care provision, further research, and implementation efforts.
Authors: Louisa G Gordon; Nicholas G Hirst; George C Mayne; David I Watson; Timothy Bright; Wang Cai; Andrew P Barbour; Bernard M Smithers; David C Whiteman; Simon Eckermann Journal: J Gastrointest Surg Date: 2012-05-30 Impact factor: 3.452
Authors: Caroline S Bennette; David L Veenstra; Anirban Basu; Laurence H Baker; Scott D Ramsey; Josh J Carlson Journal: Med Decis Making Date: 2016-03-24 Impact factor: 2.583