Decision Frameworks for Assessing Cost-Effectiveness Given Previous Nonoptimal Decisions

Introduction Economic evaluations identify the best course of action by a decision maker with respect to the level of health within the overall population. Traditionally, they identify 1 optimal treatment choice. In many jurisdictions, multiple technologies can be covered for the same heterogeneous patient population, which limits the applicability of this framework for directly determining whether a new technology should be covered. This article explores the impact of different decision frameworks within this context. Methods Three alternate decision frameworks were considered: the traditional normative framework in which only the optimal technology will be covered (normative); a commonly adopted framework in which the new technology is recommended for reimbursement only if it is optimal, with coverage of other technologies remaining as before (current); and a framework that assesses specifically whether coverage of the new technology is optimal, incorporating previous reimbursement decisions and the market share of current technologies (positivist). The implications of the frameworks were assessed using a simulated probabilistic Markov model for a chronic progressive condition. Results Results illustrate how the different frameworks can lead to different reimbursement recommendations. This in turn produces differences in population health effects and the resultant price reductions required for covering the new technology. Conclusion By covering only the optimal treatment option, decision makers can maximize the level of health across a population. If decision makers are unwilling to defund technologies, however, the second best option of adopting the positivist framework has the greatest relevance with respect to deciding whether a new technology should be covered. Highlights Traditionally, economic evaluations focus on identifying the optimal treatment choice. This paper considers three alternative decision frameworks, within the context of multiple technologies being covered for the same heterogeneous patient population. This paper highlight that if decision makers are unwilling to defund therapies, current approaches to assessing cost effectiveness may be non-optimal.