Statistical methods for cost-effectiveness analyses

Area of Science:

• Health Economics
• Biostatistics
• Clinical Trial Analysis

Background:

• Evaluating competing interventions requires robust statistical methods for cost and effect data.
• Existing cost-effectiveness (c-e) measures may not fully capture patient-level cost-effect relationships.

Purpose of the Study:

• To present a statistical framework for analyzing cost and effect data from RCTs or observational studies.
• To propose novel c-e measures that utilize patient-level cost-effect linkages.
• To provide statistical techniques for assessing intervention admissibility, equality, and ranking.

Main Methods:

• Utilizing parameters of the joint distribution of costs and effects or a regression function.
• Developing new c-e measures based on patient-level cost-effect relationships.
• Implementing a two-stage statistical procedure for intervention assessment and ranking.

Main Results:

• New c-e measures are proposed, offering different perspectives and potentially altering intervention rankings.
• Statistical techniques are provided for confidence intervals, hypothesis testing (admissibility, equality), and intervention ranking.
• The framework is illustrated using a hypothetical clinical trial of antipsychotic agents.

Conclusions:

• The proposed statistical framework and novel c-e measures enhance the evaluation of competing interventions.
• The methods facilitate statistically sound decision-making in healthcare by providing robust intervention rankings.
• This approach improves the statistical rigor in health economic evaluations, particularly in clinical settings.