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Mapping EQ-5D-3L to EQ-5D-5L
Ben A van Hout1, James W Shaw2
1University of Sheffield.
Summary
This study developed new algorithms to map the EQ-5D-3L to EQ-5D-5L value sets. The recommended method uses ordinal logistic regression with a latent factor for improved prediction accuracy.
Area of Science:
- Health Economics
- Psychometrics
- Health Outcomes Research
Background:
- The EQ-5D-3L and EQ-5D-5L are widely used health status questionnaires.
- Population value sets exist for both EQ-5D-3L and EQ-5D-5L.
- A method to link responses between the two versions is needed.
Purpose of the Study:
- To develop and validate algorithms for mapping EQ-5D-3L responses to EQ-5D-5L value sets.
- To improve the accuracy of health state valuations when using different EQ-5D versions.
Main Methods:
- Utilized data from 3580 participants who completed both EQ-5D-3L and EQ-5D-5L.
- Developed and compared various algorithms, including nonparametric and ordinal logistic regression.
- Evaluated models using Akaike's Information Criterion (AIC) and prediction error metrics.
Main Results:
- Models incorporating EQ-5D-3L dimension indicators showed the highest prediction accuracy.
- Including a latent factor improved predictive accuracy and lowered AIC.
- The developed mapping approach outperformed an alternative copula-based method.
Conclusions:
- Novel algorithms for mapping EQ-5D-3L to EQ-5D-5L value sets were developed.
- The recommended approach uses ordinal logistic regression with a latent factor.
- This method offers accurate predictions for health state valuations.
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