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Method for Calculating the Simultaneous Maximum Acceptable Risk Threshold (SMART) from Discrete-Choice Experiment
Angelyn Otteson Fairchild1, Shelby D Reed2, Juan Marcos Gonzalez2
1University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
New Simultaneous Maximum Acceptable Risk Thresholds (SMART) analysis better reflects how patients accept multiple risks bundled with treatments. This method improves understanding of risk tolerance in medical decision-making.
Area of Science:
- Decision Sciences
- Risk Analysis
- Health Economics
Background:
- Medical decisions involve balancing treatment benefits against multiple, uncertain adverse outcomes (risks).
- Conventional maximum acceptable risk (MAR) estimates often assess individual risks, potentially misrepresenting bundled risk acceptance.
- Existing methods may lead to misinterpretation of risk tolerance when multiple risks are present.
Purpose of the Study:
- To introduce and demonstrate a method for identifying multidimensional risk-tolerance measures.
- To develop a framework for evaluating the joint acceptance of multiple risks in exchange for treatment benefits.
- To compare a novel approach with conventional single-outcome MAR estimates.
Main Methods:
- Utilized simulations and a published discrete-choice experiment.
- Developed the Simultaneous Maximum Acceptable Risk Thresholds (SMART) framework.
- Analyzed the relationship between utility expectations and multiple risk probabilities.
Main Results:
- SMART identifies combinations of risks jointly accepted for specific treatment benefits.
- The utility associated with treatments involving multiple risks can be related even with independent preferences.
- The form of marginal effects of adverse-event probabilities influences the difference between SMART and conventional MAR.
Conclusions:
- SMART analysis offers a transparent and precise portrayal of multiple risk acceptance.
- Conventional MAR estimates may not accurately reflect acceptance of simultaneous risks.
- SMART should be considered in preference studies involving multiple adverse event risks.
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