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Modifying the quality-adjusted life year calculation to account for meaningful change in health-related quality of life: insights from a pragmatic clinical trial

Author

Listed:
  • Nathan S. McClure

    (University of Alberta
    University of Alberta)

  • Mike Paulden

    (University of Alberta)

  • Arto Ohinmaa

    (University of Alberta
    University of Alberta)

  • Jeffrey A. Johnson

    (University of Alberta
    University of Alberta)

Abstract

Background We propose a modified quality-adjusted life year (QALY) calculation that aims to be consistent with guidance for interpreting change in patient-reported outcomes. This calculation incorporates the minimally important difference (MID) in generic preference-based health-related quality of life (HRQL) change scores to reflect what might be considered meaningful HRQL improvement/deterioration. In doing so, we review common issues in QALY calculations such as adjustment for baseline scores and standardizing for between-group differences. Methods Using EQ-5D-5L outcome data from the Alberta TEAMCare-Primary Care Network trial in the management of depression for patients with type 2 diabetes (n = 98), this study compared results from different QALY calculation methods to investigate the impact of (i) adjusting for baseline HRQL score, (ii) standardizing between-group differences at baseline, and (iii) adjusting for ‘meaningful’ HRQL changes. The following QALY calculation methods are examined: area under curve (QALY-AUC), change from baseline (QALY-CFB), regression modelling (QALY-R), and incorporating an MID for HRQL changes from baseline (QALY-MID). Results The incremental QALY-AUC estimate favoured the Collaborative Care group (0.031) while the incremental QALY-CFB (−0.028) estimate favoured Enhanced Care. Adjusting for meaningful HRQL changes resulted in a crude incremental QALY-MID of −0.023; however, after adjusting for between-group differences at baseline, QALY-R and adjusted incremental QALY-MID estimates were −0.007 and −0.001, respectively. In addition, recursive regression analyses showed that very low baseline HRQL scores impact incremental QALY estimates. Conclusions Uncertainty in incremental QALY estimates reflects uncertainty in the value of small within-individual change as well as the impact of small differences between groups at baseline. Applying a responder-definition approach yielded crude and adjusted QALY-MID estimates that were more in favour of Collaborative Care than QALY-CFB and QALY-R estimates, respectively, suggesting that ambiguous small changes in HRQL scores have the potential to influence QALY outcomes used in economic or non-economic applications.

Suggested Citation

  • Nathan S. McClure & Mike Paulden & Arto Ohinmaa & Jeffrey A. Johnson, 2021. "Modifying the quality-adjusted life year calculation to account for meaningful change in health-related quality of life: insights from a pragmatic clinical trial," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(9), pages 1441-1451, December.
    • Handle: RePEc:spr:eujhec:v:22:y:2021:i:9:d:10.1007_s10198-021-01324-x
    DOI: 10.1007/s10198-021-01324-x
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    References listed on IDEAS

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    1. MacKillop, Eleanor & Sheard, Sally, 2018. "Quantifying life: Understanding the history of Quality-Adjusted Life-Years (QALYs)," Social Science & Medicine, Elsevier, vol. 211(C), pages 359-366.
    2. Ogorevc, Marko & Murovec, Nika & Fernandez, Natacha Bolanos & Rupel, Valentina Prevolnik, 2019. "Questioning the differences between general public vs. patient based preferences towards EQ-5D-5L defined hypothetical health states," Health Policy, Elsevier, vol. 123(2), pages 166-172.
    3. Rachael Hunter & Gianluca Baio & Thomas Butt & Stephen Morris & Jeff Round & Nick Freemantle, 2015. "An Educational Review of the Statistical Issues in Analysing Utility Data for Cost-Utility Analysis," PharmacoEconomics, Springer, vol. 33(4), pages 355-366, April.
    4. Pickles, Kristen & Lancsar, Emily & Seymour, Janelle & Parkin, David & Donaldson, Cam & Carter, Stacy M., 2019. "Accounts from developers of generic health state utility instruments explain why they produce different QALYs: A qualitative study," Social Science & Medicine, Elsevier, vol. 240(C).
    5. John Brazier & Roberta Ara & Donna Rowen & Helene Chevrou-Severac, 2017. "A Review of Generic Preference-Based Measures for Use in Cost-Effectiveness Models," PharmacoEconomics, Springer, vol. 35(1), pages 21-31, December.
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    More about this item

    Keywords

    Quality-adjusted life year; Health-related quality of life; Minimally important difference; Type 2 diabetes; Depression;
    All these keywords.

    JEL classification:

    • D63 - Microeconomics - - Welfare Economics - - - Equity, Justice, Inequality, and Other Normative Criteria and Measurement
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • I10 - Health, Education, and Welfare - - Health - - - General

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