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Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15

Author

Listed:
  • Christine Mpundu-Kaambwa

    (University of South Australia, Business School)

  • Gang Chen

    (Monash University)

  • Remo Russo

    (Flinders University
    Women’s and Children’s Hospital)

  • Katherine Stevens

    (University of Sheffield)

  • Karin Dam Petersen

    (Aalborg University)

  • Julie Ratcliffe

    (Flinders University)

Abstract

Background The Pediatric Quality of Life Inventory™ 4.0 Short Form 15 Generic Core Scales (hereafter the PedsQL) and the Child Health Utility-9 Dimensions (CHU9D) are two generic instruments designed to measure health-related quality of life in children and adolescents in the general population and paediatric patient groups living with specific health conditions. Although the PedsQL is widely used among paediatric patient populations, presently it is not possible to directly use the scores from the instrument to calculate quality-adjusted life-years (QALYs) for application in economic evaluation because it produces summary scores which are not preference-based. Objective This paper examines different econometric mapping techniques for estimating CHU9D utility scores from the PedsQL for the purpose of calculating QALYs for cost-utility analysis. Methods The PedsQL and the CHU9D were completed by a community sample of 755 Australian adolescents aged 15–17 years. Seven regression models were estimated: ordinary least squares estimator, generalised linear model, robust MM estimator, multivariate factorial polynomial estimator, beta-binomial estimator, finite mixture model and multinomial logistic model. The mean absolute error (MAE) and the mean squared error (MSE) were used to assess predictive ability of the models. Results The MM estimator with stepwise-selected PedsQL dimension scores as explanatory variables had the best predictive accuracy using MAE and the equivalent beta-binomial model had the best predictive accuracy using MSE. Conclusions Our mapping algorithm facilitates the estimation of health-state utilities for use within economic evaluations where only PedsQL data is available and is suitable for use in community-based adolescents aged 15–17 years. Applicability of the algorithm in younger populations should be assessed in further research.

Suggested Citation

  • Christine Mpundu-Kaambwa & Gang Chen & Remo Russo & Katherine Stevens & Karin Dam Petersen & Julie Ratcliffe, 2017. "Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15," PharmacoEconomics, Springer, vol. 35(4), pages 453-467, April.
    • Handle: RePEc:spr:pharme:v:35:y:2017:i:4:d:10.1007_s40273-016-0476-y
    DOI: 10.1007/s40273-016-0476-y
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    References listed on IDEAS

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    Cited by:

    1. Tosin Lambe & Emma Frew & Natalie J. Ives & Rebecca L. Woolley & Carole Cummins & Elizabeth A. Brettell & Emma N. Barsoum & Nicholas J. A. Webb, 2018. "Mapping the Paediatric Quality of Life Inventory (PedsQL™) Generic Core Scales onto the Child Health Utility Index–9 Dimension (CHU-9D) Score for Economic Evaluation in Children," PharmacoEconomics, Springer, vol. 36(4), pages 451-465, April.
    2. Asrul Akmal Shafie & Irwinder Kaur Chhabra & Jacqueline Hui Yi Wong & Noor Syahireen Mohammed, 2021. "Mapping PedsQL™ Generic Core Scales to EQ-5D-3L utility scores in transfusion-dependent thalassemia patients," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 735-747, July.
    3. Karin Dam Petersen & Gang Chen & Christine Mpundu-Kaambwa & Katherine Stevens & John Brazier & Julie Ratcliffe, 2018. "Measuring Health-Related Quality of Life in Adolescent Populations: An Empirical Comparison of the CHU9D and the PedsQLTM 4.0 Short Form 15," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(1), pages 29-37, February.

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