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Mapping the PedsQL™ onto the CHU9D: An Assessment of External Validity in a Large Community-Based Sample

Author

Listed:
  • Christine Mpundu-Kaambwa

    (University of South Australia Business School)

  • Gang Chen

    (Monash University)

  • Elisabeth Huynh

    (The Australian National University)

  • Remo Russo

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

  • Julie Ratcliffe

    (University of South Australia Business School
    Flinders University)

Abstract

Background Mapping algorithms have been indicated as a second-best solution for estimating health state utilities for the calculation of quality-adjusted life-years within cost-utility analysis when no generic preference-based measure is incorporated into the study. However, the predictive performance of these algorithms may be variable and hence it is important to assess their external validity before application in different settings. Objective The aim of this study was to assess the external validity and generalisability of existing mapping algorithms for predicting preference-based Child Health Utility 9D (CHU9D) utilities from non-preference-based Pediatric Quality of Life Inventory (PedsQL) scores among children and adolescents living with or without disabilities or health conditions. Methods Five existing mapping algorithms, three developed using data from an Australian community population and two using data from a UK population with one or more self-reported health conditions, were externally validated on data from the Longitudinal Study of Australian Children (n = 6623). The predictive accuracy of each mapping algorithm was assessed using the mean absolute error (MAE) and the mean squared error (MSE). Results Values for the MAE (0.0741–0.2302) for all validations were within the range of published estimates. In general, across all ages, the algorithms amongst children and adolescents with disabilities/health conditions (Australia MAE: 0.2085–0.2302; UK MAE: 0.0854–0.1162) performed worse relative to those amongst children and adolescents without disabilities/health conditions (Australia MAE: 0.1424–0.1645; UK MAE: 0.0741–0.0931). Conclusions The published mapping algorithms have acceptable predictive accuracy as measured by MAE and MSE. The findings of this study indicate that the choice of the most appropriate mapping algorithm to apply may vary according to the population under consideration.

Suggested Citation

  • Christine Mpundu-Kaambwa & Gang Chen & Elisabeth Huynh & Remo Russo & Julie Ratcliffe, 2019. "Mapping the PedsQL™ onto the CHU9D: An Assessment of External Validity in a Large Community-Based Sample," PharmacoEconomics, Springer, vol. 37(9), pages 1139-1153, September.
    • Handle: RePEc:spr:pharme:v:37:y:2019:i:9:d:10.1007_s40273-019-00808-2
    DOI: 10.1007/s40273-019-00808-2
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    References listed on IDEAS

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    1. Hyndman, Rob J. & Koehler, Anne B., 2006. "Another look at measures of forecast accuracy," International Journal of Forecasting, Elsevier, vol. 22(4), pages 679-688.
    2. 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.
    3. Emma Frew & Tosin Lambe, 2018. "Comment on: “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(8), pages 1029-1029, August.
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