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Published on: September 16, 2022
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A simple framework to identify optimal cost-effective risk thresholds for a single screen: Comparison to Decision
1Division of Cancer Epidemiology and Genetics, US National Cancer Institute, NIH/DHHS, Rockville MD, USA.
Summary
This study introduces a simple framework for calculating optimal risk thresholds for medical interventions, considering costs and life-years gained. It addresses limitations of Decision Curve Analysis (DCA) by providing transparent and cost-effective threshold identification.
Area of Science:
- Biomedical statistics
- Health economics
- Decision analysis
Background:
- Decision Curve Analysis (DCA) is widely used for evaluating risk models but does not incorporate costs.
- Existing full decision analyses for optimal threshold identification are often complex and difficult to understand.
Purpose of the Study:
- To develop a simple, transparent framework for calculating optimal risk thresholds for single-time screening interventions.
- To identify optimal cost-effective risk thresholds and the monetary value of life-years gained.
Main Methods:
- Developed a framework to calculate Incremental Net Benefit based on test/treatment costs and life-years gained.
- Derived simple expressions for optimal risk thresholds and the value of life-years gained.
- Applied the framework to the context of BRCA1/2 mutation screening.
Main Results:
- The proposed framework provides a straightforward method for estimating optimal risk thresholds.
- Identified that many thresholds suggested by DCA may be infeasible due to high cost per life-year gained.
- Facilitates sensitivity analyses regarding cost and effectiveness parameters.
Conclusions:
- The new framework offers a simple and transparent approach to estimating optimal risk thresholds.
- It provides critical insights into cost-effectiveness and can bridge the gap between DCA and full decision analysis.
- This method aids in making informed decisions regarding screening and interventions based on economic and clinical value.
Keywords:
AUCBRCA1BRCA2Decision CurvesDiagnostic TestingIncremental Net BenefitNet BenefitROCScreeningYouden’s indexcost effectivenessdecision analysisrisk predictionMore Related Videos
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