Stochastic cost-effectiveness analysis on population benefits.

Dealing with randomness is a crucial aspect that cost-effectiveness analysis (CEA) tools need to address, but existing stochastic CEA tools have rarely examined risk and return from the perspective of population benefits, concerning the benefits of a group of individuals but not just a typical one. This paper proposes a stochastic CEA tool that supports medical decision-making from the perspective of population benefits of risk and return, the risk-adjusted incremental cost-effectiveness ratio (ICER). The tool has a traditional form of ICER but uses the cost under a risk-adjusted expectation. Theoretically, we prove that the tool can provide medical decisions trimming that promote the risk-return level on population benefits within any intervention structure and can also serve as a criterion for the optimal intervention structure. Numerical simulations within a framework of mean–variance support the conclusions in this paper. Key points for decision makers: The typical assumption in classical CEA that all get the new intervention versus standard of care may not be the best to achieve the best outcome to population, a mixed structure can be better The intervention structure should be modified using a criterion considering slight changes on the structure of the treatment mix. Use a risk adjustment concerning cost and outcome uncertainties in taking expectation in ICER calculation gives the optimal treatment mix for population benefits. [ABSTRACT FROM AUTHOR]