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Cost-effectiveness analysis: a proposal of new reporting standards in statistical analysis
1a Division of Biostatistics and Epidemiology, Department of Public Health Sciences , University of California , Davis , California , USA.
Cost-effectiveness analysis (CEA) uses incremental cost-effectiveness ratios to compare health strategies. This study advocates for reporting mean and median ratios together for more comprehensive decision-making.
Area of Science:
- Health economics
- Biostatistics
- Decision sciences
Background:
- Cost-effectiveness analysis (CEA) is crucial for evaluating health interventions.
- Statistical complexities like skewness and censoring challenge traditional CEA methods.
- The incremental cost-effectiveness ratio (ICER) is the standard metric but has limitations.
Purpose of the Study:
- To address statistical complexities in cost-effectiveness analysis.
- To propose enhanced reporting standards for more comprehensive CEA.
- To improve informed decision-making in health strategy evaluation.
Main Methods:
- Reviewing statistical challenges in cost estimation and CEA.
- Proposing the joint reporting of mean- and median-based ICERs.
- Suggesting the inclusion of average cost-effectiveness ratios (ACERs).
Main Results:
- Current CEA methodologies face statistical hurdles.
- Reporting multiple ratio types (mean, median, average) provides a fuller picture.
- Integrating summary and inferential statistics enhances data interpretation.
Conclusions:
- Expanded perspectives and reporting standards are needed for robust CEA.
- Joint reporting of mean/median ICERs and ACERs offers complementary insights.
- Comprehensive statistical reporting supports better-informed health policy decisions.
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