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. 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 betterThe 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.