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

Lan Gao (), Wei Luo, Utsana Tonmukayakul, Marj Moodie and Gang Chen
Additional contact information
Lan Gao: Deakin University
Wei Luo: Deakin University
Utsana Tonmukayakul: Deakin University
Marj Moodie: Deakin University
Gang Chen: Monash University

The European Journal of Health Economics, 2021, vol. 22, issue 2, No 12, 350 pages

Abstract: 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.

Keywords: MacNew; EQ-5D-5L; Econometric; Machine learning; Economic evaluation (search for similar items in EconPapers)
JEL-codes: I1 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s10198-020-01259-9

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