ArticleThe European journal of health economics : HEPAC : health economics in prevention and care2021
Mapping MacNew Heart Disease Quality of Life Questionnaire onto country-specific EQ-5D-5L utility scores: a comparison of traditional regression models with a machine learning technique.
Article in The European journal of health economics : HEPAC : health economics in prevention and care, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Measurement properties of utility-based health-related quality of life measures in cardiac rehabilitation and secondary prevention programs: a systematic review.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Pooled it
- Mapping the modified rankin scale (mRS) onto the assessment of quality of life (AQoL) utilities.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2025Trial
- Mapping from SIBDQ to EQ-5D-5L for patients with inflammatory bowel disease.The European journal of health economics : HEPAC : health economics in prevention and care · 2024Trial
- Mapping of the EORTC QLQ-LC43 to EQ-5D-5 L index in patients with lung cancer: comparison of traditional regression models with machine learning technique.The European journal of health economics : HEPAC : health economics in prevention and care · 2026Article
- Improving the value of population health data for health policy and decision-making using machine learning algorithms in EQ-5D-5L index estimation.Scientific reports · 2026Article
- Predicting Health Utilities Using Health Administrative Data: Leveraging Survey-linked Health Administrative Data from Ontario, Canada.Applied health economics and health policy · 2025Article
- Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods.Health and quality of life outcomes · 2025Article
- Mapping the 12-item World Health Organization disability assessment schedule 2.0 (WHODAS 2.0) onto the assessment of quality of life (AQoL)-4D utilities.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2024Article
- China Health Related Outcomes Measures (CHROME): development of a descriptive system to support cardiovascular disease specific preference-based measure for the Chinese population.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2023Article
- Mapping the Seattle Angina Questionnaire to EQ-5D-5L in patients with coronary heart disease.Health and quality of life outcomes · 2023Article
- Mapping of disease-specific Oxford Knee Score onto EQ-5D-5L utility index in knee osteoarthritis.Journal of orthopaedic surgery and research · 2023Article
- Mapping the Memorial Anxiety Scale for Prostate Cancer to the SF-6D.Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation · 2021Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aims to derive country-specific EQ-5D-5L health status utility (HSU) from the MacNew Heart Disease Health-related Quality of Life questionnaire (MacNew) using both traditional regression analyses, as well as a machine learning technique.
methodsData were drawn from the Multi-Instrument Comparison (MIC) survey. The EQ-5D-5L was scored using 4 country-specific tariffs (United States, United Kingdom, Germany, and Canada). The traditional regression techniques, as well as a machine learning technique, deep neural network (DNN), were adopted to directly predict country-specific EQ-5D-5L HSUs (i.e. a direct mapping approach). An indirect response mapping was undertaken additionally. The optimal algorithm was identified based on three goodness-of-fit tests, namely, the mean absolute error (MAE), mean error (ME) and root mean square error (RMSE), with the first being the primary criteria. Internal validation was undertaken.
resultsIndirect response mapping and direct mapping (via betamix with MacNew items as the key predictors) were found to produce the optimal mapping algorithms with the lowest MAE when EQ-5D-5L were scored using three country-specific tariffs (United Kingdom, Canada, and Germany for the former and United Kingdom, United States, Canada and Germany for the latter approach). DNN approach generated the lowest MAE and RMSE when using the Germany-specific tariff.
conclusionsAmong different approaches been explored, there is not a conclusive conclusion regarding the optimal method for developing mapping algorithms. A machine learning approach represents an alternative mapping approach that should be explored further. The reported algorithms from response mapping have the potential to be more widely used; however, the performance needs to be externally validated.
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