Communicating Uncertainty in Economic Evaluations

Abstract

Background. In cost-effectiveness analysis (CEA), it is common to compare a single, new intervention with 1 or more existing interventions representing current practice ignoring other, unrelated interventions. Sectoral CEAs, in contrast, take a perspective in which the costs and effectiveness of all possible interventions within a certain disease area or health care sector are compared to maximize health in a society given resource constraints. Stochastic league tables (SLT) have been developed to represent uncertainty in sectoral CEAs but have 2 shortcomings: 1) the probabilities reflect inclusion of individual interventions and not strategies and 2) data on robustness are lacking. The authors developed an extension of SLT that addresses these shortcomings. Methods. Analogous to nonprobabilistic MAXIMIN decision rules, the uncertainty of the performance of strategies in sectoral CEAs may be judged with respect to worst possible outcomes, in terms of health effects obtainable within a given budget. Therefore, the authors assessed robustness of strategies likely to be optimal by performing optimization separately on all samples and on samples yielding worse than expected health benefits. The approach was tested on 2 examples, 1 with independent and 1 with correlated cost and effect data. Results. The method was applicable to the original SLT example and to a new example and provided clear and easily interpretable results. Identification of interventions with robust performance as well as the best performing strategies was straightforward. Furthermore, the robustness of strategies was assessed with a MAXIMIN decision rule. Conclusion. The SLT extension improves the comprehensibility and extends the usefulness of outcomes of SLT for decision makers. Its use is recommended whenever an SLT approach is considered.