USING STOCHASTIC MULTI-CRITERIA ACCEPTABILITY ANALYSIS TO SUPPORT DECISION MAKING ON THE REIMBURSEMENT OF MEDICAL INTERVENTIONS

Purpose: To support reimbursement decision making in settings where multiple cost and effectiveness criteria are relevant for the decision making process.

Method: In cost-effectiveness analysis, it is currently standard practice to consider a single cost criterion and a singe effectiveness criterion. For preventive and curative interventions, this latter aspect can usually be effectively dealt with by aggregating all relevant health effects into a single measure of effectiveness, such as (quality-adjusted) life years. However, for interventions that are not directly targeted at prolonging a subject's life expectancy, it is common practice to arbitrarily select one clinical outcome as the effectiveness criterion and discard all other relevant outcome measures. This introduces a selection bias that may result in sub-optimal reimbursement decisions. Stochastic multi-criteria acceptability analysis (SMAA) provides a framework for transparent and replicable analysis of complex decision problems involving uncertainty in both the criteria measurements and the values of the weights that reflect the relative importance of the different criteria. The approach has previously been applied to support drug market approval decisions. In addition, the currently popular net monetary benefit (NMB) framework is as a special case of the more general SMAA method. To illustrate how SMAA can be applied to support reimbursement decision making in settings where there are more than two relevant criteria, we applied the method in a case study related to infertility treatment.

Result: Seven in-vitro fertilization (IVF) strategies were evaluated in terms of three criteria: costs, probability of a life birth, and risk of a twin pregnancy. When conducting a classical analysis by ignoring the risk of a twin pregnancy, four strategies were clearly not cost-effective, but it was difficult to choose among the three remaining strategies. When conducting the SMAA analysis by including the risk of a twin pregnancy, the former strategies still had very low probabilities of being cost-effective. However, the SMAA analysis showed that there where clear trade-offs among the three remaining strategies.

Conclusion: Compared to the classical analysis based on the NMB framework, the SMAA analysis resulted in increased discrimination among the three remaining treatment strategies. The method should therefore be considered a welcome addition to the toolkit of the applied health economist.