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An examination of machine learning to map non‐preference based patient reported outcome measures to health state utility values

Author

Listed:
  • Mona Aghdaee
  • Bonny Parkinson
  • Kompal Sinha
  • Yuanyuan Gu
  • Rajan Sharma
  • Emma Olin
  • Henry Cutler

Abstract

Non‐preference‐based patient‐reported outcome measures (PROMs) are popular in health outcomes research. These measures, however, cannot be used to estimate health state utilities, limiting their usefulness for economic evaluations. Mapping PROMs to a multi‐attribute utility instrument is one solution. While mapping is commonly conducted using econometric techniques, failing to specify the complex interactions between variables may lead to inaccurate prediction of utilities, resulting in inaccurate estimates of cost‐effectiveness and suboptimal funding decisions. These issues can be addressed using machine learning. This paper evaluates the use of machine learning as a mapping tool. We adopt a comprehensive approach to compare six machine learning techniques with eight econometric techniques to map the Patient‐Reported Outcomes Measurement Information System Global Health 10 (PROMIS‐GH10) to the EuroQol five dimensions (EQ‐5D‐5L). Using data collected from 2015 Australians, we find the least absolute shrinkage and selection operator (LASSO) model out‐performed all machine learning techniques and the adjusted limited dependent variable mixture model (ALDVMM) out‐performed all econometric techniques, with the LASSO performing better than ALDVMM. The variable selection feature of LASSO was then used to enhance the performance of the ALDVMM in a hybrid model. Our analysis identifies the potential benefits and challenges of using machine learning techniques for mapping and offers important insights for future research.

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  • 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.
  • Handle: RePEc:wly:hlthec:v:31:y:2022:i:8:p:1525-1557
    DOI: 10.1002/hec.4503
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    References listed on IDEAS

    as
    1. Noémi Kreif & Richard Grieve & Iván Díaz & David Harrison, 2015. "Evaluation of the Effect of a Continuous Treatment: A Machine Learning Approach with an Application to Treatment for Traumatic Brain Injury," Health Economics, John Wiley & Sons, Ltd., vol. 24(9), pages 1213-1228, September.
    2. Chris Schilling & Duncan Mortimer & Kim Dalziel, 2017. "Using CART to Identify Thresholds and Hierarchies in the Determinants of Funding Decisions," Medical Decision Making, , vol. 37(2), pages 173-182, February.
    3. Akash Malhotra, 2021. "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 11(3), pages 549-581, September.
    4. Mónica Hernández Alava & Allan Wailoo & Fred Wolfe & Kaleb Michaud, 2014. "A Comparison of Direct and Indirect Methods for the Estimation of Health Utilities from Clinical Outcomes," Medical Decision Making, , vol. 34(7), pages 919-930, October.
    5. Powell, James L., 1984. "Least absolute deviations estimation for the censored regression model," Journal of Econometrics, Elsevier, vol. 25(3), pages 303-325, July.
    6. Chris Schilling & Duncan Mortimer & Kim Dalziel & Emma Heeley & John Chalmers & Philip Clarke, 2016. "Using Classification and Regression Trees (CART) to Identify Prescribing Thresholds for Cardiovascular Disease," PharmacoEconomics, Springer, vol. 34(2), pages 195-205, February.
    7. Stavros Petrou & Oliver Rivero-Arias & Helen Dakin & Louise Longworth & Mark Oppe & Robert Froud & Alastair Gray, 2015. "Preferred Reporting Items for Studies Mapping onto Preference-Based Outcome Measures: The MAPS Statement," Applied Health Economics and Health Policy, Springer, vol. 13(5), pages 437-443, October.
    8. Laura A. Gray & Mónica Hernández Alava, 2018. "A command for fitting mixture regression models for bounded dependent variables using the beta distribution," Stata Journal, StataCorp LP, vol. 18(1), pages 51-75, March.
    9. Patrick W. Sullivan & Vahram Ghushchyan, 2006. "Mapping the EQ-5D Index from the SF-12: US General Population Preferences in a Nationally Representative Sample," Medical Decision Making, , vol. 26(4), pages 401-409, July.
    10. Ralph Crott & Andrew Briggs, 2010. "Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 11(4), pages 427-434, August.
    11. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    12. Hal R. Varian, 2014. "Big Data: New Tricks for Econometrics," Journal of Economic Perspectives, American Economic Association, vol. 28(2), pages 3-28, Spring.
    13. 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.
    14. Fan Yang & Nancy Devlin & Nan Luo, 2019. "Impact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(1), pages 99-105, February.
    15. 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.
    16. Ben Kearns & Roberta Ara & Allan Wailoo & Andrea Manca & Monica Alava & Keith Abrams & Mike Campbell, 2013. "Good Practice Guidelines for the use of Statistical Regression Models in Economic Evaluations," PharmacoEconomics, Springer, vol. 31(8), pages 643-652, August.
    17. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    18. 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.
    19. Sungchul Park & Anirban Basu, 2018. "Alternative evaluation metrics for risk adjustment methods," Health Economics, John Wiley & Sons, Ltd., vol. 27(6), pages 984-1010, June.
    20. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    21. Jeffrey C. Chen & Abe Dunn & Kyle Hood & Alexander Driessen & Andrea Batch, 2019. "Off to the Races: A Comparison of Machine Learning and Alternative Data for Predicting Economic Indicators," NBER Chapters, in: Big Data for Twenty-First-Century Economic Statistics, pages 373-402, National Bureau of Economic Research, Inc.
    22. 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.
    23. Monica Hernandez Alava & Allan Wailoo, 2015. "Fitting adjusted limited dependent variable mixture models to EQ-5D," Stata Journal, StataCorp LP, vol. 15(3), pages 737-750, September.
    24. 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.
    25. 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.
    26. Manning, Willard G. & Mullahy, John, 2001. "Estimating log models: to transform or not to transform?," Journal of Health Economics, Elsevier, vol. 20(4), pages 461-494, July.
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