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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

Author

Listed:
  • Lan Gao

    (Deakin University)

  • Wei Luo

    (Deakin University)

  • Utsana Tonmukayakul

    (Deakin University)

  • Marj Moodie

    (Deakin University)

  • Gang Chen

    (Monash University)

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.

Suggested Citation

  • Lan Gao & Wei Luo & Utsana Tonmukayakul & Marj Moodie & Gang Chen, 2021. "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," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(2), pages 341-350, March.
    • Handle: RePEc:spr:eujhec:v:22:y:2021:i:2:d:10.1007_s10198-020-01259-9
    DOI: 10.1007/s10198-020-01259-9
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    References listed on IDEAS

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    1. John Brazier & Yaling Yang & Aki Tsuchiya & Donna Rowen, 2010. "A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(2), pages 215-225, April.
    2. Ron D. Hays & Dennis A. Revicki & David Feeny & Peter Fayers & Karen L. Spritzer & David Cella, 2016. "Using Linear Equating to Map PROMIS® Global Health Items and the PROMIS-29 V2.0 Profile Measure to the Health Utilities Index Mark 3," PharmacoEconomics, Springer, vol. 34(10), pages 1015-1022, October.
    3. Gang Chen & Munir A. Khan & Angelo Iezzi & Julie Ratcliffe & Jeff Richardson, 2016. "Mapping between 6 Multiattribute Utility Instruments," Medical Decision Making, , vol. 36(2), pages 160-175, February.
    4. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    5. Billingsley Kaambwa & Gang Chen & Julie Ratcliffe & Angelo Iezzi & Aimee Maxwell & Jeff Richardson, 2017. "Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs)," PharmacoEconomics, Springer, vol. 35(1), pages 111-124, January.
    6. Nicolas R. Thompson & Brittany R. Lapin & Irene L. Katzan, 2017. "Mapping PROMIS Global Health Items to EuroQol (EQ-5D) Utility Scores Using Linear and Equipercentile Equating," PharmacoEconomics, Springer, vol. 35(11), pages 1167-1176, November.
    7. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, March.
    8. Kristina Ludwig & J.-Matthias Graf von der Schulenburg & Wolfgang Greiner, 2018. "German Value Set for the EQ-5D-5L," PharmacoEconomics, Springer, vol. 36(6), pages 663-674, June.
    9. Anirban Basu & Andrea Manca, 2012. "Regression Estimators for Generic Health-Related Quality of Life and Quality-Adjusted Life Years," Medical Decision Making, , vol. 32(1), pages 56-69, January.
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 22nd March 2021
      by Chris Sampson in The Academic Health Economists' Blog on 2021-03-22 12:00:01

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    More about this item

    Keywords

    MacNew; EQ-5D-5L; Econometric; Machine learning; Economic evaluation;
    All these keywords.

    JEL classification:

    • I1 - Health, Education, and Welfare - - Health

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