, 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



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.


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.


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.


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.


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.


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.



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.


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)


  1. 1.
    Brazier J, Ratcliffe J, Tsuchiya A, Salomon J. Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press; 2007.Google Scholar
  2. 2.
    Fontaine KR, Barofsky I. Obesity and health-related quality of life. Obes Rev. 2001;2(3):173–82.CrossRefPubMedGoogle Scholar
  3. 3.
    Drummond MF, Sculpher MJ, Torrance GW, O’Brien BJ, Stoddart GL. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press; 2005.Google Scholar
  4. 4.
    Harris A, Bulfone L. Getting value for money: “The Australian experience”. In: International M-H, Jost T, editors. Health care coverage determinations: an international comparative study. Maidenhead: Open University Press; 2004.Google Scholar
  5. 5.
    National Institute for Health and Care Excellence. Guide to the methods of technology appraisal. National Health Service. 2010.Google Scholar
  6. 6.
    Varni JW, Burwinkle TM, Seid M, Skarr D. The PedsQL 4.0 as a pediatric population health measure: feasibility, reliability, and validity. Ambul Pediatr. 2003;3(6):329–41.CrossRefPubMedGoogle Scholar
  7. 7.
    Brazier JE, Yang Y, Tsuchiya A, Rowen DL. A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur J Health Econ. 2010;11(2):215–25. doi: 10.1007/s10198-009-0168-z.CrossRefPubMedGoogle Scholar
  8. 8.
    Chen G, Stevens K, Rowen D, Ratcliffe J. From KIDSCREEN-10 to CHU9D: creating a unique mapping algorithm for application in economic evaluation. Health Qual Life Outcomes. 2014;12:134. doi: 10.1186/s12955-014-0134-z.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Furber G, Segal L, Leach M, Cocks J. Mapping scores from the Strengths and Difficulties Questionnaire (SDQ) to preference-based utility values. Qual Life Res. 2014;23(2):403–11. doi: 10.1007/s11136-013-0494-6.CrossRefPubMedGoogle Scholar
  10. 10.
    Khan KA, Petrou S, Rivero-Arias O, Walters SJ, Boyle SE. Mapping EQ-5D utility scores from the PedsQL generic core scales. Pharmacoeconomics. 2014;32(7):693–706. doi: 10.1007/s40273-014-0153-y.CrossRefPubMedGoogle Scholar
  11. 11.
    Payakachat N, Tilford JM, Kuhlthau KA, van Exel NJ, Kovacs E, Bellando J, et al. Predicting health utilities for children with autism spectrum disorders. Autism Res. 2014;7(6):649–63. doi: 10.1002/aur.1409.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Varni JW, Seid M, Kurtin PS. PedsQL 4.0: reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Med Care. 2001;39(8):800–12.CrossRefPubMedGoogle Scholar
  13. 13.
    Stevens K. Assessing the performance of a new generic measure of health-related quality of life for children and refining it for use in health state valuation. Appl Health Econ Health Policy. 2011;9(3):157–69. doi: 10.2165/11587350-000000000-00000.CrossRefPubMedGoogle Scholar
  14. 14.
    Stevens K. Valuation of the Child Health Utility 9D Index. Pharmacoeconomics. 2012;30(8):729–47. doi: 10.2165/11599120-000000000-00000.CrossRefPubMedGoogle Scholar
  15. 15.
    Ratcliffe J, Flynn T, Terlich F, Stevens K, Brazier J, Sawyer M. Developing adolescent-specific health state values for economic evaluation: an application of profile case best-worst scaling to the Child Health Utility 9D. Pharmacoeconomics. 2012;30(8):713–27. doi: 10.2165/11597900-000000000-00000.CrossRefPubMedGoogle Scholar
  16. 16.
    Ratcliffe J, Huynh E, Chen G, Stevens K, Swait J, Brazier J, et al. Valuing the child health utility 9D: using profile case best worst scaling methods to develop a new adolescent specific scoring algorithm. Soc Sci Med. 2016;157:48–59. doi: 10.1016/j.socscimed.2016.03.042.CrossRefPubMedGoogle Scholar
  17. 17.
    Boyce W, Torsheim T, Currie C, Zambon A. The Family Affluence Scale as a measure of national wealth: validation of an adolescent self-report measure. Soc Indic Res. 2006;78(3):473–87.CrossRefGoogle Scholar
  18. 18.
    Stevens K, Ratcliffe J. Measuring and valuing health benefits for economic evaluation in adolescence: an assessment of the practicality and validity of the child health utility 9D in the Australian adolescent population. Value Health. 2012;15(8):1092–9. doi: 10.1016/j.jval.2012.07.011.CrossRefPubMedGoogle Scholar
  19. 19.
    Ratcliffe J, Stevens K, Flynn T, Brazier J, Sawyer M. An assessment of the construct validity of the CHU9D in the Australian adolescent general population. Qual Life Res. 2012;21(4):717–25. doi: 10.1007/s11136-011-9971-y.CrossRefPubMedGoogle Scholar
  20. 20.
    Chen G, Flynn T, Stevens K, Brazier J, Huynh E, Sawyer M, et al. Assessing the health-related quality of life of Australian adolescents: an empirical comparison of the child health utility 9D and EQ-5D-Y instruments. Value Health. 2015;18(4):432–8. doi: 10.1016/j.jval.2015.02.014.CrossRefPubMedGoogle Scholar
  21. 21.
    Petrou S, Rivero-Arias O, Dakin H, Longworth L, Oppe M, Froud R, et al. The MAPS reporting statement for studies mapping onto generic preference-based outcome measures: explanation and elaboration. PharmacoEconomics. 2015;33(10):993–1011. doi: 10.1007/s40273-015-0312-9.CrossRefPubMedGoogle Scholar
  22. 22.
    Boers M, Verhoeven AC, Markusse HM, van de Laar MA, Westhovens R, van Denderen JC, et al. Randomised comparison of combined step-down prednisolone, methotrexate and sulphasalazine with sulphasalazine alone in early rheumatoid arthritis. Lancet. 1997;350(9074):309–18. doi: 10.1016/s0140-6736(97)01300-7.CrossRefPubMedGoogle Scholar
  23. 23.
    International CLL-IPI Working Group. An international prognostic index for patients with chronic lymphocytic leukaemia (CLL-IPI): a meta-analysis of individual patient data. Lancet Oncol. 2016;17(6):779–90. doi: 10.1016/s1470-2045(16)30029-8.CrossRefGoogle Scholar
  24. 24.
    Chappell LC, Seed PT, Myers J, Taylor RS, Kenny LC, Dekker GA, et al. Exploration and confirmation of factors associated with uncomplicated pregnancy in nulliparous women: prospective cohort study. Bmj. 2013;347:f6398. doi: 10.1136/bmj.f6398.CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Kuk D, Varadhan R. Model selection in competing risks regression. Stat Med. 2013;32(18):3077–88. doi: 10.1002/sim.5762.CrossRefPubMedGoogle Scholar
  26. 26.
    Allen LA, Yager JE, Funk MJ, Levy WC, Tulsky JA, Bowers MT, et al. Discordance between patient-predicted and model-predicted life expectancy among ambulatory heart failure patients. JAMA J Am Med Assoc. 2008;299(21):2533–42. doi: 10.1001/jama.299.21.2533.CrossRefGoogle Scholar
  27. 27.
    StataCorp. Stata Statistical Software: Release 14. 2015.Google Scholar
  28. 28.
    Dakin H. Review of studies mapping from quality of life or clinical measures to EQ-5D: an online database. Health Qual Life Outcomes. 2013;11:151. doi: 10.1186/1477-7525-11-151.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Longworth L, Rowen D. Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value Health. 2013;16(1):202–10. doi: 10.1016/j.jval.2012.10.010.CrossRefPubMedGoogle Scholar
  30. 30.
    Hernandez Alava M, Wailoo A, Wolfe F, Michaud K. A comparison of direct and indirect methods for the estimation of health utilities from clinical outcomes. Med Decis Making. 2014;34(7):919–30. doi: 10.1177/0272989x13500720.CrossRefPubMedGoogle Scholar
  31. 31.
    Huang IC, Frangakis C, Atkinson MJ, Willke RJ, Leite WL, Vogel WB, et al. Addressing ceiling effects in health status measures: a comparison of techniques applied to measures for people with HIV disease. Health Serv Res. 2008;43(1 Pt 1):327–39. doi: 10.1111/j.1475-6773.2007.00745.x.PubMedPubMedCentralGoogle Scholar
  32. 32.
    Payakachat N, Summers KH, Pleil AM, Murawski MM, Thomas J 3rd, Jennings K, et al. Predicting EQ-5D utility scores from the 25-item National Eye Institute Vision Function Questionnaire (NEI-VFQ 25) in patients with age-related macular degeneration. Qual Life Res. 2009;18(7):801–13. doi: 10.1007/s11136-009-9499-6.CrossRefPubMedGoogle Scholar
  33. 33.
    Gujarati DN. Basic econometrics. 4th ed. Boston. Mass. London: McGraw-Hill; 2003.Google Scholar
  34. 34.
    Chen G, Khan MA, Iezzi A, Ratcliffe J, Richardson J. Mapping between 6 multiattribute utility instruments. Med Decis Making. 2016;36(2):160–75. doi: 10.1177/0272989x15578127.CrossRefPubMedGoogle Scholar
  35. 35.
    McCullagh P, Nelder JA. Generalized linear models. 2nd ed. London: Chapman & Hall; 1989.CrossRefGoogle Scholar
  36. 36.
    Royston P, Sauerbrei W. Multivariable modeling with cubic regression splines: a principled approach. Stata J. 2007;7(1):45–70.Google Scholar
  37. 37.
    Briggs A, Sculpher M, Claxton K. Decision modelling for health economic evaluation. Oxford: Oxford University Press; 2006.Google Scholar
  38. 38.
    Ospina R, Ferrari SL. A general class of zero-or-one inflated beta regression models. Comput Stat Data Anal. 2012;56(6):1609–23.CrossRefGoogle Scholar
  39. 39.
    Basu A, Manca A. Regression estimators for generic health-related quality of life and quality-adjusted life years. Med Decis Making. 2012;32(1):56–69. doi: 10.1177/0272989x11416988.CrossRefPubMedGoogle Scholar
  40. 40.
    Khan I, Morris S. A non-linear beta-binomial regression model for mapping EORTC QLQ-C30 to the EQ-5D-3L in lung cancer patients: a comparison with existing approaches. Health Qual Life Outcomes. 2014;12(1):1–16. doi: 10.1186/s12955-014-0163-7.CrossRefGoogle Scholar
  41. 41.
    Everitt B, Hand D. Finite mixture distributions. London and New York: Chapman and Hall; 1981.CrossRefGoogle Scholar
  42. 42.
    McLachlan G, Peel D. Finite mixture models. New York: Wiley; 2000.CrossRefGoogle Scholar
  43. 43.
    Kent S, Gray A, Schlackow I, Jenkinson C, McIntosh E. Mapping from the Parkinson’s disease questionnaire PDQ-39 to the generic EuroQol EQ-5D-3L: the value of mixture models. Med Decis Making. 2015;35(7):902–11. doi: 10.1177/0272989x15584921.CrossRefPubMedGoogle Scholar
  44. 44.
    Deb P. Finite mixture models. 2008. Accessed 11 Sept 2016.
  45. 45.
    Gray AM, Rivero-Arias O, Clarke PM. Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Med Decis Making. 2006;26(1):18–29. doi: 10.1177/0272989x05284108.CrossRefPubMedGoogle Scholar
  46. 46.
    Le QA, Doctor JN. Probabilistic mapping of descriptive health status responses onto health state utilities using Bayesian networks: an empirical analysis converting SF-12 into EQ-5D utility index in a national US sample. Med Care. 2011;49(5):451–60. doi: 10.1097/MLR.0b013e318207e9a8.CrossRefPubMedGoogle Scholar
  47. 47.
    Koch GG. Intraclass Correlation Coefficient.  Encyclopedia of Statistical Sciences. John Wiley & Sons, Inc.; 2004. doi: 10.1002/0471667196.ess1275
  48. 48.
    Hyndman RJ, Koehler AB. Another look at measures of forecast accuracy. Int J Forecast. 2006;22(4):679–88. doi: 10.1016/j.ijforecast.2006.03.001.CrossRefGoogle Scholar
  49. 49.
    Shcherbakov MV, Brebels B, Shcherbakova NL, Tyukov AP, Janovsky TA, Kamae VA. A survey of forecast error measures. World Appl Sci J 24 (Information Technologies in Modern Industry, Education and Society). 2013;24(24):171–6.Google Scholar
  50. 50.
    Wong CK, Lam CL, Rowen D, McGhee SM, Ma KP, Law WL, et al. Mapping the functional assessment of cancer therapy-general or -colorectal to SF-6D in Chinese patients with colorectal neoplasm. Value Health. 2012;15(3):495–503. doi: 10.1016/j.jval.2011.12.009.CrossRefPubMedGoogle Scholar
  51. 51.
    Wu EQ, Mulani P, Farrell MH, Sleep D. Mapping FACT-P and EORTC QLQ-C30 to patient health status measured by EQ-5D in metastatic hormone-refractory prostate cancer patients. Value Health. 2007;10(5):408–14. doi: 10.1111/j.1524-4733.2007.00195.x.CrossRefPubMedGoogle Scholar
  52. 52.
    Petrou S, Rivero-Arias O, Dakin H, Longworth L, Oppe M, Froud R, et al. Preferred reporting items for studies mapping onto preference-based outcome measures: the MAPS statement. Qual Life Res. 2016;25(2):275–81. doi: 10.1007/s11136-015-1082-8.CrossRefPubMedGoogle Scholar
  53. 53.
    Chuang LH, Whitehead SJ. Mapping for economic evaluation. Br Med Bull. 2012;101:1–15. doi: 10.1093/bmb/ldr049.CrossRefPubMedGoogle Scholar
  54. 54.
    Pinedo-Villanueva RA, Turner D, Judge A, Raftery JP, Arden NK. Mapping the Oxford hip score onto the EQ-5D utility index. Qual Life Res. 2013;22(3):665–75. doi: 10.1007/s11136-012-0174-y.CrossRefPubMedGoogle Scholar
  55. 55.
    Tsuchiya A, Brazier JE, McColl E, Parkin D. Deriving preference-based single indices from non-preference based condition-specific instruments: Converting AQLQ into EQ5D indices Sheffield Health Economics Group Discussion Paper Series. 2002; Ref 02/1.Google Scholar
  56. 56.
    Brennan DS, Spencer AJ. Mapping oral health related quality of life to generic health state values. BMC Health Serv Res. 2006;6:96. doi: 10.1186/1472-6963-6-96.CrossRefPubMedPubMedCentralGoogle Scholar
  57. 57.
    Sauerland S, Weiner S, Dolezalova K, Angrisani L, Noguera CM, Garcia-Caballero M, et al. Mapping utility scores from a disease-specific quality-of-life measure in bariatric surgery patients. Value Health. 2009;12(2):364–70. doi: 10.1111/j.1524-4733.2008.00442.x.CrossRefPubMedGoogle Scholar
  58. 58.
    Bansback N, Marra C, Tsuchiya A, Anis A, Guh D, Hammond T, et al. Using the health assessment questionnaire to estimate preference-based single indices in patients with rheumatoid arthritis. Arthritis Rheum. 2007;57(6):963–71. doi: 10.1002/art.22885.CrossRefPubMedGoogle Scholar
  59. 59.
    Australian Demographic Statistics, 2015, ‘Table 8: Estimated resident population, by age and sex—at 30 June 2015’, data cube: Excel spreadsheet, cat. no. 31010do002_201512 [database on the Internet]. Australian Bureau of Statistics 2015. Available from: Accessed: 6 Sept 2016.
  60. 60.
    Australian Institute of Health and Welfare. Young Australians: their health and wellbeing 2011 (Cat. no. PHE 140). Canberra, Australia: Australian Institute of Health and Welfare; 2011.Google Scholar
  61. 61.
    Manning WG, Mullahy J. Estimating log models: to transform or not to transform? J Health Econ. 2001;20(4):461–94CrossRefPubMedGoogle Scholar

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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