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Shedding new light onto the ceiling and floor? A quantile regression approach to compare EQ-5D and SF-6D responses

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Author Info

  • Janelle Seymour

    (Primary Care Research Unit (PCRU), Department of General Practice, University of Melbourne, Australia)

  • Paul McNamee

    (Health Economics Research Unit (HERU), University of Aberdeen, UK)

  • Anthony Scott

    (Melbourne Institute of Applied Economic and Social Research, University of Melbourne, Australia)

  • Michela Tinelli

    (Health Economics Research Unit (HERU), University of Aberdeen, UK)

Abstract

An important issue in the measurement of health status concerns the extent to which an instrument displays lack of sensitivity to changes in health status at the extremes of the distribution, known as floor and ceiling effects. Previous studies use relatively simple methods that focus on the mean of the distribution to examine these effects. The aim of this paper is to determine whether quantile regression using longitudinal data improves our understanding of the relationship between quality of life instruments. The study uses EQ-5D and SF-36 (converted to SF-6D values) instruments with both baseline and follow-up data. Relative to ordinary least least-squares (OLS), a first difference model shows much lower association between the measures, suggesting that OLS methods may lead to biased estimates of the association, due to unobservable patient characteristics. The novel finding, revealed by quantile regression, is that the strength of association between the instruments is different across different parts of the health distribution, and is dependent on whether health improves or deteriorates. The results suggest that choosing one instrument at the expense of another is difficult without good prior information surrounding the expected magnitude and direction of health improvement related to a health-care intervention. Copyright © 2009 John Wiley & Sons, Ltd.

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File URL: http://hdl.handle.net/10.1002/hec.1505
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Bibliographic Info

Article provided by John Wiley & Sons, Ltd. in its journal Health Economics.

Volume (Year): 19 (2010)
Issue (Month): 6 ()
Pages: 683-696

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Handle: RePEc:wly:hlthec:v:19:y:2010:i:6:p:683-696

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Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/5749

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  1. John Brazier & Jennifer Roberts & Aki Tsuchiya & Jan Busschbach, 2004. "A comparison of the EQ-5D and SF-6D across seven patient groups," Health Economics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 13(9), pages 873-884.
  2. Winkelmann, Rainer, 2006. "Reforming health care: Evidence from quantile regressions for counts," Journal of Health Economics, Elsevier, Elsevier, vol. 25(1), pages 131-145, January.
  3. John Brazier & Mark Deverill, 1999. "A checklist for judging preference-based measures of health related quality of life: Learning from psychometrics," Health Economics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 8(1), pages 41-51.
  4. Bernie J. O'Brien & Marian Spath & Gordon Blackhouse & J.L. Severens & Paul Dorian & John Brazier, 2003. "A view from the bridge: agreement between the SF-6D utility algorithm and the Health Utilities Index," Health Economics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 12(11), pages 975-981.
  5. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, American Economic Association, vol. 15(4), pages 143-156, Fall.
  6. Richard Holland & Richard D Smith & Ian Harvey & Louise Swift & Elizabeth Lenaghan, 2004. "Assessing quality of life in the elderly: a direct comparison of the EQ-5D and AQoL," Health Economics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 13(8), pages 793-805.
  7. Karen Gerard & Tricia Nicholson & Mark Mullee & Raj Mehta & Paul Roderick, 2004. "EQ-5D versus SF-6D in an Older, Chronically Ill Patient Group," Applied Health Economics and Health Policy, Springer Healthcare | Adis, Springer Healthcare | Adis, vol. 3(2), pages 91-102.
  8. Brazier, John & Roberts, Jennifer & Deverill, Mark, 2002. "The estimation of a preference-based measure of health from the SF-36," Journal of Health Economics, Elsevier, Elsevier, vol. 21(2), pages 271-292, March.
  9. Eric W. Christensen, 2004. "Scale and scope economies in nursing homes: A quantile regression approach," Health Economics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 13(4), pages 363-377.
  10. Louise Longworth & Stirling Bryan, 2003. "An empirical comparison of EQ-5D and SF-6D in liver transplant patients," Health Economics, John Wiley & Sons, Ltd., John Wiley & Sons, Ltd., vol. 12(12), pages 1061-1067.
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Cited by:
  1. Lene Lunde, 2013. "Can EQ-5D and 15D be used interchangeably in economic evaluations? Assessing quality of life in post-stroke patients," The European Journal of Health Economics, Springer, Springer, vol. 14(3), pages 539-550, June.
  2. Gunasekara, Fiona Imlach & Carter, Kristie & Blakely, Tony, 2012. "Comparing self-rated health and self-assessed change in health in a longitudinal survey: Which is more valid?," Social Science & Medicine, Elsevier, Elsevier, vol. 74(7), pages 1117-1124.

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