Bayesian cost-effectiveness analysis with two measures of effectiveness: the cost-effectiveness acceptability plane.

Area of Science:

• Health Economics
• Biostatistics
• Clinical Research Methodology

Background:

• Cost-effectiveness analysis (CEA) is crucial for comparing healthcare technologies.
• A lack of consensus exists regarding the optimal measure of effectiveness in CEA.
• The choice of effectiveness measure significantly impacts CEA conclusions.

Purpose of the Study:

• To present a Bayesian framework for conducting CEA with multiple effectiveness measures.
• To analyze CEA with both binary and continuous effectiveness measures.
• To introduce the cost-effectiveness acceptability plane (CEAP) for enhanced decision-making.

Main Methods:

• Developed a Bayesian framework for CEA incorporating multiple effectiveness measures.
• Extended cost-effectiveness acceptability curves to create the cost-effectiveness acceptability plane (CEAP).
• Validated the framework using data from two clinical trials (HIV treatments and methadone programs).

Main Results:

• The Bayesian framework accommodates multiple effectiveness measures, including binary and continuous outcomes.
• The cost-effectiveness acceptability plane (CEAP) provides a suitable measure for decision-making with multiple outcomes.
• Validation demonstrated the framework's applicability in real-world clinical trial settings.

Conclusions:

• The proposed Bayesian framework offers a robust method for CEA with multiple effectiveness measures.
• The CEAP is a valuable tool for comparing healthcare interventions with diverse outcomes.
• This approach enhances the rigor and clarity of decision-making in health economic evaluations.