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Mapping of the EORTC QLQ-C30 to EQ-5D-5L index in patients with lymphomas

Author

Listed:
  • Richard Huan Xu

    (The Chinese University of Hong Kong
    Centre for Health Systems and Policy Research, The Chinese University of Hong Kong)

  • Eliza Lai Yi Wong

    (The Chinese University of Hong Kong
    Centre for Health Systems and Policy Research, The Chinese University of Hong Kong)

  • Jun Jin

    (Tsinghua University)

  • Ying Dou

    (Tsinghua University)

  • Dong Dong

    (The Chinese University of Hong Kong
    Centre for Health Systems and Policy Research, The Chinese University of Hong Kong
    Shenzhen Research Institute, The Chinese University of Hong Kong)

Abstract

Objective The objective of this study was to develop algorithms to map the EORTC QLQ-C30 (QLQ-C30) onto EQ-5D-5L in a sample of patients with lymphomas. Methods An online nationwide survey of patients with lymphoma was carried out in China. Ordinary least squares (OLS), beta-based mixture, adjusted limited dependent variable mixture regression, and a Tobit regression model were used to develop the mapping algorithms. The QLQ-C30 subscales/items, their squared and interaction terms, and respondents’ demographic variables were used as independent variables. The root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) were estimated based on tenfold cross-validation to assess the predictive ability of the selected models. Results Data of 2222/4068 respondents who self-completed the online survey were elicited for analyses. The mean EQ-5D-5L index score was 0.81 (SD 0.21, range − 0.81–1.0). 19.98% of respondents reported an index score at 1.0. In total, 72 models were generated based on four regression methods. According to the RMSE, MAE and R2, the OLS model including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables showed the best fit for overall and the Non-Hodgkin’s lymphoma sample; for Hodgkin’s lymphoma, the ALDVMM with 1-component model, including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables, showed a better fit than the other models. Conclusion The mapping algorithms enable the EQ-5D-5L index scores to be predicted by QLQ-C30 subscale/item scores with good precision in patients living with lymphomas.

Suggested Citation

  • Richard Huan Xu & Eliza Lai Yi Wong & Jun Jin & Ying Dou & Dong Dong, 2020. "Mapping of the EORTC QLQ-C30 to EQ-5D-5L index in patients with lymphomas," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 21(9), pages 1363-1373, December.
  • Handle: RePEc:spr:eujhec:v:21:y:2020:i:9:d:10.1007_s10198-020-01220-w
    DOI: 10.1007/s10198-020-01220-w
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    References listed on IDEAS

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    1. Carlos King Ho Wong & Prudence Wing Hang Cheung & Dino Samartzis & Keith Dip-Kei Luk & Kenneth M C Cheung & Cindy Lo Kuen Lam & Jason Pui Yin Cheung, 2017. "Mapping the SRS-22r questionnaire onto the EQ-5D-5L utility score in patients with adolescent idiopathic scoliosis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-12, April.
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    4. 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.
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    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 9th November 2020
      by Chris Sampson in The Academic Health Economists' Blog on 2020-11-09 12:00:00

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    More about this item

    Keywords

    EORTC QLQ-C30; EQ-5D-5L; Mapping algorithm; Lymphoma; China;
    All these keywords.

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