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Mapping clinical outcomes to generic preference-based outcome measures: development and comparison of methods
Mónica Hernández Alava1, Allan Wailoo1, Stephen Pudney1
1School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK.
Health Technology Assessment (Winchester, England)
|July 3, 2020
Summary
New mapping methods improve cost-effectiveness analysis by accurately linking clinical outcomes to health benefits. These flexible mixture models offer reliable results, overcoming limitations of traditional approaches like linear regression.
Area of Science:
- Health economics and outcomes research
- Statistical modeling in healthcare
- Preference-based health measures
Background:
- Cost-effectiveness analysis (CEA) commonly uses quality-adjusted life-years (QALYs) for decision-making.
- Clinical studies often lack preference-based measures needed for QALY calculation.
- Mapping methods estimate relationships between clinical outcomes and preference-based measures but can yield biased results.
Purpose of the Study:
- To develop and evaluate new and existing mapping methods for estimating health benefits.
- To test mapping method performance across diverse preference-based measures and clinical conditions.
- To develop methods for mapping directly between different preference-based measures.
Main Methods:
- Utilized 15 datasets for mapping from non-preference-based to preference-based measures (e.g., EQ-5D, SF-6D, HUI3).
- Developed mixture-model-based direct mapping approaches and indirect methods for multidirectional mapping.
- Employed copulas for flexible bivariate distribution modeling between EQ-5D versions.
Main Results:
- Proposed criteria for assessing model performance; linear regression was found inappropriate for mapping.
- Flexible direct mapping methods using mixture models demonstrated strong performance across all preference-based measures.
- Minimum of three components and disease severity covariates are crucial for accurate mapping; indirect methods showed promise for comparing preference-based measures.
Conclusions:
- Appropriate mapping methods are essential for reliable cost-effectiveness estimates.
- Flexible mixture-model-based approaches are suitable for all preference-based measures, outperforming traditional methods.
- Methodological choices, like the number of mixture components, require careful consideration on a case-by-case basis.
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