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PharmacoEconomics

, Volume 35, Issue 4, pp 453–467 | Cite as

Mapping CHU9D Utility Scores from the PedsQLTM 4.0 SF-15

  • Christine Mpundu-KaambwaEmail author
  • Gang Chen
  • Remo Russo
  • Katherine Stevens
  • Karin Dam Petersen
  • Julie Ratcliffe
Original Research Article

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.

Keywords

Mean Square Error Ordinary Little Square Mapping Algorithm Mean Absolute Error Finite Mixture Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We would like to thank all the study participants who generously gave up their time to participate in this study.

Author contributions

CMK analysed the data, interpreted the results, wrote the first draft and will act as a guarantor for the work. GC formulated the idea for the study, oversaw the design and collection of data, analysed the data, interpreted the results and made critical revisions to the manuscript. RR interpreted the results and made critical revisions to the manuscript. KS formulated the idea for the study, oversaw the design and collection of data, interpreted the results and made critical revisions to the manuscript. KDP interpreted the results and made critical revisions to the manuscript. JR formulated the idea for the study, oversaw the design and collection of data, interpreted the results and made critical revisions to the manuscript. All authors approved the final draft.

Compliance with Ethical Standards

Ethical approval for this study was obtained from the Social and Behavioural Research Ethics Committee, Flinders University (Project Number 5508).

Competing interests

CMK, GC, RR, KS, KDP and JR declare that they have no conflict of interest.

Funding

This study was funded by an Australian NHMRC Project Grant (Grant Number 1021899) entitled ‘Adolescent values for the economic evaluation of adolescent health care treatment and preventive programs’.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

40273_2016_476_MOESM1_ESM.docx (20 kb)
Supplementary material 1 (DOCX 20 kb)

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Christine Mpundu-Kaambwa
    • 1
    Email author
  • Gang Chen
    • 2
  • Remo Russo
    • 3
    • 4
  • Katherine Stevens
    • 5
  • Karin Dam Petersen
    • 6
  • Julie Ratcliffe
    • 7
  1. 1.Institute for ChoiceUniversity of South Australia, Business SchoolAdelaideAustralia
  2. 2.Centre for Health Economics, Monash Business SchoolMonash UniversityMelbourneAustralia
  3. 3.Faculty of Health Sciences, School of MedicineFlinders UniversityAdelaideAustralia
  4. 4.Department of Paediatric RehabilitationWomen’s and Children’s HospitalAdelaideAustralia
  5. 5.Health Economics and Decision ScienceUniversity of SheffieldSheffieldUnited Kingdom
  6. 6.Department of Business and Management, Faculty of Social SciencesAalborg UniversityAalborg EastDenmark
  7. 7.Flinders Health Economics GroupFlinders UniversityAdelaideAustralia

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