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Net-benefit regression with censored cost-effectiveness data from randomized or observational studies
Shuai Chen1, Jeffrey S Hoch2,3
1Division of Biostatistics, Department of Public Health Sciences, University of California, Davis, Davis, California, USA.
Statistics in Medicine
|June 6, 2022
Summary
New methods address censored cost-effectiveness data, crucial for evaluating medical interventions. These techniques enable valid analysis and subgroup identification, improving cost-effectiveness research for survival time and quality-adjusted survival time.
Area of Science:
- Health economics
- Biostatistics
- Medical decision making
Background:
- Cost-effectiveness analysis (CEA) is vital for assessing new medical interventions.
- Standard survival analysis methods are often invalid for censored cost-effectiveness data due to dependent censoring.
- Accurate analysis of censored data is essential for reliable healthcare intervention evaluation.
Purpose of the Study:
- To propose valid statistical methods for analyzing censored cost-effectiveness data.
- To enable covariate-adjustment and subgroup identification in comparative intervention studies.
- To facilitate the construction of cost-effectiveness acceptability curves with censored data.
Main Methods:
- Utilized the net-benefit regression framework for censored cost-effectiveness data.
- Developed a doubly robust estimator for average causal incremental net benefit.
- Conducted extensive numerical studies and illustrated methods with real-world survival data.
Main Results:
- Proposed methods provide a valid approach for cost-effectiveness analysis with censored data.
- The net-benefit regression framework allows for covariate adjustment and subgroup analysis.
- The doubly robust estimator enhances the validity of inferences in observational studies.
Conclusions:
- The developed methods offer a robust solution for handling censored data in cost-effectiveness evaluations.
- These techniques improve the reliability of cost-effectiveness acceptability curves and subgroup analyses.
- The findings are applicable to both survival time and quality-adjusted survival time measures.
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