IDEAS home Printed from https://ideas.repec.org/a/spr/eujhec/v22y2021i2d10.1007_s10198-020-01259-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10198-020-01259-9
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10198-020-01259-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LLC, edition 2, number long2, January.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Isa Maria Steiner & Bernd Bokemeyer & Tom Stargardt, 2024. "Mapping from SIBDQ to EQ-5D-5L for patients with inflammatory bowel disease," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 25(3), pages 539-548, April.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mona Aghdaee & Bonny Parkinson & Kompal Sinha & Yuanyuan Gu & Rajan Sharma & Emma Olin & Henry Cutler, 2022. "An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values," Health Economics, John Wiley & Sons, Ltd., vol. 31(8), pages 1525-1557, August.
    2. Admassu N. Lamu, 2020. "Does linear equating improve prediction in mapping? Crosswalking MacNew onto EQ-5D-5L value sets," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(6), pages 903-915, August.
    3. Iris Buder & Cathleen Zick & Norman Waitzman, 2020. "The Contribution of Physical Activity to Health-Related Quality of Life: New Population Estimates from National Survey Data," Applied Research in Quality of Life, Springer;International Society for Quality-of-Life Studies, vol. 15(1), pages 55-71, March.
    4. Fan Yang & Carlos K. H. Wong & Nan Luo & James Piercy & Rebecca Moon & James Jackson, 2019. "Mapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(8), pages 1195-1206, November.
    5. Verhagen, Mark D., 2021. "A Pragmatist's Guide to Prediction in the Social Sciences," SocArXiv tjkcy, Center for Open Science.
    6. Jinsuk Yang & Qing Hao & Mahmut Yaşar, 2023. "Institutional investors and cross‐border mergers and acquisitions: The 2000–2018 period," International Review of Finance, International Review of Finance Ltd., vol. 23(3), pages 553-583, September.
    7. repec:rza:wpaper:227 is not listed on IDEAS
    8. Clara Berridge & Yuanjin Zhou & Julie M. Robillard & Jeffrey Kaye, 2023. "AI Companion Robot Data Sharing: Preferences of an Online Cohort and Policy Implications," Journal of Elder Policy, John Wiley & Sons, vol. 2(3), pages 19-54, June.
    9. Dixon, Huw D. & Grimme, Christian, 2022. "State-dependent or time-dependent pricing? New evidence from a monthly firm-level survey: 1980–2017," European Economic Review, Elsevier, vol. 150(C).
    10. Melanie Lefevre, 2011. "Willingness-to-pay for Local Milk-based Dairy Product in Senegal," CREPP Working Papers 1108, Centre de Recherche en Economie Publique et de la Population (CREPP) (Research Center on Public and Population Economics) HEC-Management School, University of Liège.
    11. Erik Stam & Roy Thurik & Peter van der Zwan, 2010. "Entrepreneurial exit in real and imagined markets," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 19(4), pages 1109-1139, August.
    12. Miriam Marcén & Marina Morales, 2019. "Live together: does culture matter?," Review of Economics of the Household, Springer, vol. 17(2), pages 671-713, June.
    13. Juergen Deppner & Marcelo Cajias, 2024. "Accounting for Spatial Autocorrelation in Algorithm-Driven Hedonic Models: A Spatial Cross-Validation Approach," The Journal of Real Estate Finance and Economics, Springer, vol. 68(2), pages 235-273, February.
    14. Jeff Round & Mike Paulden, 2018. "Incorporating equity in economic evaluations: a multi-attribute equity state approach," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 19(4), pages 489-498, May.
    15. Bruno Amable, 2009. "The Differentiation of Social Demands in Europe. The Social Basis of the European Models of Capitalism," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 91(3), pages 391-426, May.
    16. Altorjai, Szilvia, 2013. "Over-qualification of immigrants in the UK," ISER Working Paper Series 2013-11, Institute for Social and Economic Research.
    17. Naguib, Costanza, 2019. "Estimating the Heterogeneous Impact of the Free Movement of Persons on Relative Wage Mobility," Economics Working Paper Series 1903, University of St. Gallen, School of Economics and Political Science.
    18. Michelsen, Carl Christian & Madlener, Reinhard, 2016. "Switching from fossil fuel to renewables in residential heating systems: An empirical study of homeowners' decisions in Germany," Energy Policy, Elsevier, vol. 89(C), pages 95-105.
    19. Philippe Goulet Coulombe & Maxime Leroux & Dalibor Stevanovic & Stéphane Surprenant, 2022. "How is machine learning useful for macroeconomic forecasting?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(5), pages 920-964, August.
    20. Rolando Rubilar-Torrealba & Karime Chahuán-Jiménez & Hanns de la Fuente-Mella & Mercedes Marzo-Navarro, 2022. "Econometric Modeling to Measure the Social and Economic Factors in the Success of Entrepreneurship," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    21. Susanne Meyer & Javier Revilla Diez, 2015. "One country, two systems: How regional institutions shape governance modes in the greater Pearl River Delta, China," Papers in Regional Science, Wiley Blackwell, vol. 94(4), pages 891-900, November.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:eujhec:v:22:y:2021:i:2:d:10.1007_s10198-020-01259-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.