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A regression framework for a probabilistic measure of cost-effectiveness.
Nicholas Illenberger1, Nandita Mitra1, Andrew J Spieker2
1Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Health Economics
|April 23, 2022
Summary
This study introduces a new regression method to analyze cost-effectiveness, identifying factors influencing treatment benefits for better health policy decisions and resource allocation.
Area of Science:
- Health Economics
- Biostatistics
- Clinical Effectiveness Research
Background:
- Informed health policy requires evaluating both cost and clinical effectiveness of treatments.
- Net benefit separation (NBS) is a novel probabilistic measure of cost-effectiveness.
- Understanding factors influencing NBS is crucial for population-level resource allocation.
Purpose of the Study:
- To introduce a regression framework for NBS to estimate covariate-specific NBS.
- To identify determinants of variation in NBS.
- To investigate associations between patient characteristics and cost-effectiveness.
Main Methods:
- Developed a regression framework for NBS.
- Employed inverse probability weighting for informative cost censoring.
- Utilized a semiparametric standardization procedure to address confounding.
- Validated the approach through simulations.
Main Results:
- NBS regression performs well in various common scenarios.
- The method can estimate covariate-specific NBS.
- Identified determinants of NBS variation.
Conclusions:
- The proposed NBS regression framework is a valuable tool for health policy analysis.
- It aids in understanding how patient factors influence treatment cost-effectiveness.
- Applicable to real-world scenarios, such as comparing cancer treatments.
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