Value-of-Information Analysis to Reduce Decision Uncertainty Associated with the Choice of Thromboprophylaxis after Total Hip Replacement in the Irish Healthcare Setting.

Abstract

Background: The National Centre for Pharmacoeconomics, in collaboration with the Health Services Executive, considers the cost effectiveness of all new medicines introduced into Ireland. Health Technology Assessments (HTAs) are conducted in accordance with the existing agreed Irish HTA guidelines. These guidelines do not specify a formal analysis of value of information (VOI). Objective: The aim of this study was to demonstrate the benefits of using VOI analysis in decreasing decision uncertainty and to examine the viability of applying these techniques as part of the formal HTA process for reimbursement purposes within the Irish healthcare system. Method: The evaluation was conducted from the Irish health payer perspective. A lifetime model evaluated the cost effectiveness of rivaroxaban, dabigatran etexilate and enoxaparin sodium for the prophylaxis of venous thromboembolism after total hip replacement. The expected value of perfect information (EVPI) was determined directly from the probabilistic analysis (PSA). Population-level EVPI (PEVPI) was determined by scaling up the EVPI according to the decision incidence. The expected value of perfect parameter information (EVPPI) was calculated for the three model parameter subsets: probabilities, preference weights and direct medical costs. Results: In the base-case analysis, rivaroxaban dominated both dabigatran etexilate and enoxaparin sodium. PSA indicated that rivaroxaban had the highest probability of being the most cost-effective strategy over a threshold range of &U20AC;0-&U20AC;100 000 per QALY. At a threshold of &U20AC;45 000 per QALY, the probability that rivaroxaban was the most cost-effective strategy was 67%. At a threshold of &U20AC;45 000 per QALY, assuming a 10-year decision time horizon, the PEVPI was &U20AC;11.96 million and the direct medical costs subset had the highest EVPPI value (&U20AC;9.00 million at a population level). In order to decrease uncertainty, a more detailed costing study was undertaken. In the subsequent analysis, rivaroxaban continued to dominate both comparators. In the PSA, rivaroxaban continued to have the highest probability of being optimal over the threshold range &U20AC;0-&U20AC;100 000 per QALY. At &U20AC;45 000 per QALY, the probability that rivaroxaban was the most cost-effective strategy increased to 80%. At &U20AC;45 000 per QALY, the 10-year PEVPI decreased to &U20AC;3.58 million and the population value associated with the direct medical costs fell to &U20AC;1.72 million. Conclusion: This increase in probability of cost effectiveness, coupled with a substantially reduced potential opportunity loss could influence a decision maker's confidence in making a reimbursement decision. On discussions with the decision maker we now intend to incorporate the use of VOI into our HTA process.