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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

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Abstract

Background

This 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.

Methods

Data 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.

Results

Indirect 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.

Conclusions

Among 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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Availability of data and materials

MIC dataset is available upon request.

Code availability

Code for DNN model is publicly assessable.

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Funding

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Authors and Affiliations

  1. Deakin Health Economics, Institute for Health Transformation, Deakin University, Geelong, VIC, Australia

    Lan Gao, Utsana Tonmukayakul & Marj Moodie

  2. School of Information Technology, Deakin University, Geelong, VIC, Australia

    Wei Luo

  3. Centre for Health Economics, Monash University, Caulfield East, VIC, Australia

    Gang Chen

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  1. Lan Gao
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  2. Wei Luo
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  3. Utsana Tonmukayakul
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  4. Marj Moodie
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  5. Gang Chen
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Contributions

LG, WL and GC conceived the project; LG, WL and UT conducted the data analysis; LG, WL, UT, MM and GC interpreted the results; LG drafted the manuscript and all the other authors reviewed and approved the final version for submission.

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Correspondence to Lan Gao.

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Gao, L., Luo, W., Tonmukayakul, U. et al. 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. Eur J Health Econ 22, 341–350 (2021). https://doi.org/10.1007/s10198-020-01259-9

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