Stochastic cost-effectiveness analysis on population benefits

Area of Science:

• Health economics
• Decision analysis
• Risk management

Background:

• Existing cost-effectiveness analysis (CEA) tools often overlook population-level risk and return.
• Current methods focus on individual benefits, not group outcomes.
• Addressing randomness is critical for robust CEA.

Purpose of the Study:

• To propose a new stochastic CEA tool for medical decision-making.
• To evaluate risk and return from the perspective of population benefits.
• To introduce the risk-adjusted incremental cost-effectiveness ratio (ICER).

Main Methods:

• Development of a novel stochastic CEA tool.
• Incorporation of risk-adjusted expectation for cost calculations.
• Theoretical proof of decision-making capabilities.
• Numerical simulations using a mean-variance framework.

Main Results:

• The proposed tool supports medical decisions by optimizing risk-return levels for population benefits.
• The risk-adjusted ICER can guide the selection of optimal intervention structures.
• Theoretical proofs and simulations validate the tool's effectiveness.

Conclusions:

• The novel stochastic CEA tool effectively addresses population benefits in risk and return analysis.
• The risk-adjusted ICER provides a criterion for optimal medical intervention strategies.
• This approach enhances the robustness of CEA in healthcare settings.