Skip to main content

Advertisement

SpringerLink
Log in

Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences

  • Original Paper
  • Published:
The European Journal of Health Economics Aims and scope Submit manuscript

Abstract

Background

Although cancer-specific Health-Related Quality of Life (HRQOL) are commonly included in randomized clinical trials or other prospective non-randomized clinical studies, it is rare that preference-based instruments are used that allow the calculation of a utility weight suitable for estimating quality-adjusted life-years gained.

Objective

To develop a mapping algorithm to transform the EORTC QLQ-C30 questionnaire responses into EQ-5D derived utilities.

Study design

Retrospective data analysis of a multicentre, multicountry prospective clinical trial in breast cancer patients.

Methods

Regression analysis of individual pairs of EQ-5D and QLQ-C30 scores.

Results

A model that explained 80% of the variance was developed to estimate EQ-5D Utilities from QLQ-C30 scores at individual level. From this reliable group level means and deviations can be derived.

Conclusions

Mapping from QLQ-C30 scores to EQ-5D-derived utilities when only QLQ-C30 data are available has been shown to be possible with good accuracy. Validation of the proposed algorithm in other external clinical datasets should be encouraged.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Notes

  1. Because we did not have access to the clinical data aside from the QOL data, it was not possible to further assess the responses by patients’ characteristics. However, in order to keep the practical application of the mapping algorithm across patient samples as general as possible, one does not want to include in the mapping equation any socio-demographic variables when considering mapping.

  2. Utilities were transformed to Disutilities because this results in a right-skewed distribution bounded by zero on the left. This changes only the sign of the coefficients in the regressions.

  3. The data provided to us were extracted from the cleaned and frozen clinical trial database from the EORTC, raising the suspicion of the existence of genuine outliers.

References

  1. Efficace, F., Bottomley, A., Osoba, D., Gotay, C., Flechtner, H., D’haese, S., Zurlo, A.: Beyond the development of health-related quality-of-life (HRQOL) measures: a checklist for evaluating HRQOL outcomes in cancer clinical trials—does HRQOL evaluation in prostate cancer research inform clinical decision making? J. Clin. Oncol. 21(18), 3502–3511 (2003)

    Article  Google Scholar 

  2. Fayers, P.M., Aaronson, N.K., Bjordal, K., Groenvold, M., Curran, D., Bottomley, A., on behalf of the EORTC Quality of Life Group: The EORTC QLQ-C30 scoring manual, 3rd edn. European Organisation for Research and Treatment of Cancer (EORTC), Brussels (2001)

    Google Scholar 

  3. Gotay, C.C., Kawamoto, C.T., Bottomley, A., Efficace, F.: The prognostic significance of patient-reported outcomes in cancer clinical trials. J. Clin. Oncol. 26(8), 1355–1363 (2008)

    Article  Google Scholar 

  4. Therasse, P., Mauriac, L., Welnicka-Jaskiewicz, M., Bruning, P., Cufer, T., Bonnefoi, H., Tomiak, E., Pritchard, K.I., Hamilton, A., Piccart, M.J.: Final results of a randomized phase III trial comparing cyclophosphamide, epirubicin, and fluorouracil with a dose-Intensified epirubicin and cyclophosphamide–filgrastim as neoadjuvant treatment in locally advanced breast cancer: an EORTC-NCIC-SAKK multicenter study. J. Clin. Oncol. 21(5), 843–850 (2003)

    Article  Google Scholar 

  5. EQ-5D Value sets: inventory, comparative review and user guide. In: Szende, A., Oppe, M., Devlin, N. (Eds.) EuroQol Group Monographs. Springer, Dordrecht (2007), ISBN 978-1402055102

  6. Efficace, F., Therasse, P., Piccart, M.J., Coens, C., Van Steen, K., Welnicka-Jaskiewicz, M., Cufer, T., Dyczka, J., Lichinitser, M., Shepherd, L., de Haes, H., Sprangers, M.A., Bottomley, A.: Health-related quality of life parameters as prognostic factors in a nonmetastatic breast cancer population: an international multicenter study. J. Clin. Oncol. 22(16), 3381–3388 (2004)

    Article  Google Scholar 

  7. Bottomley, A., Therasse, P., Piccart, M., Efficace, F., Coens, C., Gotay, C., Welnicka-Jaskiewicz, M., Dyczka, L.M.J., Cufer, T., Lichinitser, M.R., Schornagel, J.H., Bonnefoi, H., Shepherd, L., For the European Organisation for Researchand Treatment of Cancer Breast Cancer Group, the National Cancer Institute of Canada, Swiss Group for Clinical Cancer Research: Health-related quality of life in survivors of locally advanced breast cancer: an international randomised controlled phase III trial. Lancet Oncol 6, 287–294 (2005)

    Article  Google Scholar 

  8. http://groups.eortc.be/qol/questionnaires_modules.htm

  9. Dolan, P.: Modeling valuations for EuroQol health states. Med. Care 35(11), 1095–1108 (1997)

    Article  Google Scholar 

  10. Dolan, P., Gudex, C., Kind, P., Williams, A.: The time trade-off method: results from a general population study. Health Econ. 5, 141–154 (1996)

    Article  Google Scholar 

  11. Szende, A., Oppe, M., Devlin, N.: EQ-5D value sets, inventory, comparative review and user guide. Springer, Berlin (2006)

    Google Scholar 

  12. McKenzie, L., Van der Pol, M.: Mapping the EORTC QLQ C-30 onto the EQ-5D instrument: the potential to estimate QALY’s without generic preference data. Value Health 12(1), 167–171 (2009)

    Article  Google Scholar 

  13. Gray, A.M., Rivero-Arias, O., Clarke, P.M.: Estimating the association between SF-12 responses and EQ-5D utility values by response mapping. Med. Decis. Mak. 26, 18–29 (2006)

    Article  Google Scholar 

  14. Brazier, J.E., Yang, Y., Tsuchiya, A., Rowen, D.L.: A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. Eur. J. Health Econ. (2009). doi:10.1007/s10198-009-0168-z

  15. Peat, J., Barton, B.: Medical statistics: a guide to data analysis and critical appraisal, 1st edn., p. 150. BMJ Books, Oxford (2005)

  16. Hardin, J.W., Hilbe, J.M.: Generalized estimation equations. Chapman & Hall/CRC, Boca Raton (2003)

  17. Doornebosch, P.G., Tollenaar, R.A., Gosselink, M.P., Stassen, L.P., Dijkhuis, C.M., Schouten, W.R., van de Velde, C.J., de Graaf, E.J.: Quality of life after transanal endoscopic microsurgery and total Mesorectal excision in early rectal cancer. Colorectal Dis. 9(6), 553–558 (2007)

    Article  Google Scholar 

  18. Gosselink, M.P., Busschbach, J.J., Dijkhuis, C.M., Stassen, L.P., Hop, W.C., Schouten, W.R.: Quality of life after total mesorectal excision for rectal cancer. Colorectal Dis. 8(1), 15–22 (2006)

    Article  Google Scholar 

  19. Krabbe, P.F., Peerenboom, L., Langenhoff, B.S., Ruers, T.J.: Responsiveness of the generic EQ-5D summary measure compared to the disease-specific EORTC QLQ C-30. Qual. Life Res. 13(7), 1247–1253 (2004)

    Article  Google Scholar 

  20. Krahn, M., Bremner, K.E., Tomlinson, G., Ritvo, P., Irvine, J., Naglie, G.: Responsiveness of disease-specific and generic utility instruments in prostate cancer patients. Qual. Life Res. 16(3), 509–522 (2007)

    Article  Google Scholar 

  21. Wu, E.Q., Mulani, P., Farrell, M.H., 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 10(5), 408–414 (2007)

    Article  Google Scholar 

  22. Kind, P.: Measuring the quality of life on cancer: an index based on QLQ-C30 presentation, ASCO 2005 Annual Meeting, Category Health Services Research, abstract published in JCO, vol. 23, No. 16S, Part I of II, June 1 Supplement, 2005:6013

  23. Kontodimopoulos, N., Aletras, V.H., Paliouras, D.: Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D and 15D instruments. Value Health, 12(8), 1151–1157 (2009)

    Google Scholar 

  24. McKenzie, L., Van der Pol, M.: Mapping the EORTC QLQ C-30 onto the EQ-5D instrument: the potential to estimate QALY’s without generic preference DATA. Value Health 12(1), 167–171 (2009)

    Article  Google Scholar 

  25. Pickard, A.S., Shaw, J.W., Lin, H.W., Trask, P.C., Aaronson, N., Lee, T.A., Cella, D.: A patient-based utility measure of health for clinical trials of cancer therapy based on the European Organization for the Research and Treatment of Cancer Quality of Life Questionnaire. Value Health 12(6), 977–988 (2009)

    Article  Google Scholar 

  26. Jang, R.W., Mittmann, N., Isogai, P., Bradbury, P.A., Leighl, N.B.: Derivation of utility values from EORTC QLQ-C30 values in lung cancer, ISPOR 2008, Toronto (Poster abstract PCN78). Value Health 11(3), A77 (2008)

  27. Petrillo, J., Cairns, J.: Concerting condition-specific measures into preference-based outcomes for use in economic evaluation. Expert Rev. Pharmacoecon. Outcomes Res. 8(5), 453–461 (2008)

    Google Scholar 

  28. Bleeker, S.E., Moll, H.A., Steyerberg, E.W., Donders, A.R., Derksen-Lubsen, G., Grobbee, D.E., Moons, K.G.: External validation is necessary in prediction research: a clinical example. J. Clin. Epidemiol. 56(9), 826–832 (2003)

    Article  Google Scholar 

  29. Steyerberg, E.W., Bleeker, S.E., Moll, H.A., Grobbee, D.E., Moons, K.G.: Internal and external validation of predictive models: a simulation study of bias and precision in small samples. J. Clin. Epidemiol. 56(5), 441–447 (2003)

    Article  Google Scholar 

  30. Luo, N., Johnson, J.A., Shaw, J.W., Coons, S.J.: A comparison of the EQ-5D index scores derived from the US and UK population-based scoring functions. Med. Decis. Mak. 27, 321–326 (2007)

    Article  Google Scholar 

  31. Chay, K.Y., Powell, J.L.: Semiparametric censored regression models. J. Econ. Perspect. 15(4), 29–42 (2001)

    Article  Google Scholar 

  32. Sullivan, P., Ghushchyan, V.: Preference-based EQ-5D index scores for chronic conditions in the United States. Med. Decis. Mak. 26, 410–420 (2006)

    Article  Google Scholar 

Download references

Acknowledgments

This work was funded in part by an unrestricted grant from York Health Economics Consortium Ltd. The funding agreement ensured that the author’s independence in designing the study, interpreting the data, writing and publishing the report. We wish to thank Corinne O’Dowd for assisting in the cleaning and preparation of the data. We also are grateful to the EORTC Breast Cancer Group for providing access to the original trial data. The authors have no conflicts of interest that are directly relevant to the context of this study.

Author information

Authors and Affiliations

  1. Academic Hospital St Luc, Catholic University of Louvain, 10 Avenue Hippocrate, Brussels, 1200, Belgium

    Ralph Crott

  2. Section of Public Health and Health, Division of Community Based Sciences, Faculty of Medicine, University of Glasgow, Glasgow, UK

    Andrew Briggs

Authors
  1. Ralph Crott
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Andrew Briggs
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ralph Crott.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Crott, R., Briggs, A. Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences. Eur J Health Econ 11, 427–434 (2010). https://doi.org/10.1007/s10198-010-0233-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10198-010-0233-7

Keywords

JEL Classification

Search

Navigation