Skip to main content

Advertisement

SpringerLink
Log in

Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Purpose

To determine the external validity of existing mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses and to establish their generalizability in different types of cancer.

Methods

A main analysis (pooled) sample of 3560 observations (1727 patients) and two disease severity patient samples (496 and 93 patients) with repeated observations over time from Cancer 2015 were used to validate the existing algorithms. Errors were calculated between observed and predicted EQ-5D-3L utility values using a single pooled sample and ten pooled tumour type-specific samples. Predictive accuracy was assessed using mean absolute error (MAE) and standardized root-mean-squared error (RMSE). The association between observed and predicted EQ-5D utility values and other covariates across the distribution was tested using quantile regression. Quality-adjusted life years (QALYs) were calculated using observed and predicted values to test responsiveness.

Results

Ten ‘preferred’ mapping algorithms were identified. Two algorithms estimated via response mapping and ordinary least-squares regression using dummy variables performed well on number of validation criteria, including accurate prediction of the best and worst QLQ-C30 health states, predicted values within the EQ-5D tariff range, relatively small MAEs and RMSEs, and minimal differences between estimated QALYs. Comparison of predictive accuracy across ten tumour type-specific samples highlighted that algorithms are relatively insensitive to grouping by tumour type and affected more by differences in disease severity.

Conclusions

Two of the ‘preferred’ mapping algorithms suggest more accurate predictions, but limitations exist. We recommend extensive scenario analyses if mapped utilities are used in cost-utility analyses.

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
Fig. 3

Similar content being viewed by others

Notes

  1. More recently, the EQ-5D-5L has been developed to improve the instrument’s sensitivity and reduce ceiling effects commonly observed when using the EQ-5D-3L [7]. This version of the EQ-5D has five levels for each item (no problems, slight problems, moderate problems, severe problems, and extreme problems). To date, no research has mapped a non-preference-based instrument to the EQ-5D-5L.

  2. There are several versions of the QLQ-C30 questionnaire: the current version 3 differs from version 1; in that, it has four-point scales for the Physical and Role Functioning items (items 1–7) instead of two-point scales [1]. Version 2 differs from version 3 only in the number of scale points in the Physical Functioning items (items 1–5) [27].

  3. The cohort protocol stated that patients were to be followed up at 6, 12, and 24 months after enrolment, and every 12 months thereafter; however, advanced cancer (more severe) patients were fast-tracked with an early follow-up at 3 months in addition to the time points above. The follow-up time points are adhered to ± month, where practically possible.

  4. For items 1–5 in the Cancer 2015, dataset responses greater than or equal to 3 were assumed to be ‘Yes’ and responses less than or equal to 2 were assumed to be ‘No’.

  5. These tariffs each result in different theoretical ranges of utility values between 1 (full health) and some lower value equal to a negative number (worse than dead), where a value of zero represents dead (e.g. UK tariff has a theoretical range of −0.594 to 1).

  6. Standardized by dividing RMSE by the maximal tariff EQ-5D range (e.g. for UK ((1.594) × 100); minimum values: UK = −0.594, US = −0.104, NL = −0.329, KR = −0.171.

References

  1. Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for research and treatment of cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. Journal of the National Cancer Institute, 85(5), 365–376.

    Article  CAS  PubMed  Google Scholar 

  2. Cella, D. F., Tulsky, D. S., Gray, G., Sarafian, B., Linn, E., Bonomi, A., et al. (1993). The functional assessment of cancer therapy scale: Development and validation of the general measure. Journal of Clinical Oncology, 11(3), 570–579.

    CAS  PubMed  Google Scholar 

  3. Brooks, R. (1996). EuroQol: The current state of play. Health Policy, 37(1), 53–72.

    Article  CAS  PubMed  Google Scholar 

  4. Brazier, J. E., & Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42(9), 851–859.

    Article  PubMed  Google Scholar 

  5. Feeny, D., Furlong, W., Torrance, G. W., Goldsmith, C. H., Zhu, Z., DePauw, S., et al. (2002). Multiattribute and single-attribute utility functions for the health utilities index mark 3 system. Medical Care, 40(2), 113–128.

    Article  PubMed  Google Scholar 

  6. Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European Journal of Health Economics: HEPAC: Health Economics in Prevention and Care, 11(2), 215–225.

    Article  PubMed  Google Scholar 

  7. Herdman, M., Gudex, C., Lloyd, A., Janssen, M., Kind, P., Parkin, D., et al. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. McKenzie, L., & van der Pol, M. (2009). Mapping the EORTC QLQ C-30 onto the EQ-5D instrument: The potential to estimate QALYs without generic preference data. Value in Health : The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 12(1), 167–171.

    Article  Google Scholar 

  9. Kontodimopoulos, N., Aletras, V. H., Paliouras, D., & Niakas, D. (2009). Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Value in Health : The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 12(8), 1151–1157.

    Article  Google Scholar 

  10. Jang, R. W., Isogai, P. K., Mittmann, N., Bradbury, P. A., Shepherd, F. A., Feld, R., & Leighl, N. B. (2010). Derivation of utility values from European Organization for research and treatment of cancer quality of life-core 30 questionnaire values in lung cancer. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer, 5(12), 1953–1957.

    Article  Google Scholar 

  11. Crott, R., & Briggs, A. (2010). Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences. The European Journal of Health Economics : HEPAC: Health Economics in Prevention and Care, 11(4), 427–434.

    Article  PubMed  Google Scholar 

  12. Versteegh, M. M., Rowen, D., Brazier, J. E., & Stolk, E. A. (2010). Mapping onto Eq-5 D for patients in poor health. Health and Quality of Life Outcomes, 8, 141.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Kim, E. J., Ko, S. K., & Kang, H. Y. (2012). Mapping the cancer-specific EORTC QLQ-C30 and EORTC QLQ-BR23 to the generic EQ-5D in metastatic breast cancer patients. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 21(7), 1193–1203.

    Article  Google Scholar 

  14. Versteegh, M. M., Leunis, A., Luime, J. J., Boggild, M., Uyl-de Groot, C. A., & Stolk, E. A. (2012). Mapping QLQ-C30, HAQ, and MSIS-29 on EQ-5D. Medical Decision Making: An International Journal of the Society for Medical Decision Making, 32(4), 554–568.

    Article  Google Scholar 

  15. Kim, S. H., Jo, M. W., Kim, H. J., & Ahn, J. H. (2012). Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients. Health and Quality of Life Outcomes, 10, 151.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Longworth, L., Yang, Y., Young, T., Mulhern, B., Hernandez Alava, M., Mukuria, C., et al. (2014). Use of generic and condition-specific measures of health-related quality of life in NICE decision-making: A systematic review, statistical modelling and survey. Health Technology Assessment, 18(9), 1–224.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Proskorovsky, I., Lewis, P., Williams, C. D., Jordan, K., Kyriakou, C., Ishak, J., & Davies, F. E. (2014). Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma. Health and Quality of Life Outcomes, 12(1), 35.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Crott, R., Versteegh, M., & Uyl-de-Groot, C. (2013). An assessment of the external validity of mapping QLQ-C30 to EQ-5D preferences. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 22(5), 1045–1054.

    Article  Google Scholar 

  19. Rowen, D., Young, T., Brazier, J., & Gaugris, S. (2012). Comparison of generic, condition-specific, and mapped health state utility values for multiple myeloma cancer. Value in Health: The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 15(8), 1059–1068.

    Article  Google Scholar 

  20. Crott, R. (2014). Mapping algorithms from QLQ-C30 to EQ-5D utilities: No firm ground to stand on yet. Expert Review of Pharmacoecon and Outcomes Research, 14(4), 569–576.

    Article  Google Scholar 

  21. Arnold, D. T., Rowen, D., Versteegh, M. M., Morley, A., Hooper, C. E., & Maskell, N. A. (2015). Testing mapping algorithms of the cancer-specific EORTC QLQ-C30 onto EQ-5D in malignant mesothelioma. Health and Quality of Life Outcomes, 13, 6.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Altman, D. G., & Royston, P. (2000). What do we mean by validating a prognostic model? Statistics in Medicine, 19(4), 453–473.

    Article  CAS  PubMed  Google Scholar 

  23. Justice, A. C., Covinsky, K. E., & Berlin, J. A. (1999). Assessing the generalizability of prognostic information. Annals of Internal Medicine, 130(6), 515–524.

    Article  CAS  PubMed  Google Scholar 

  24. Steyerberg, E. W., Bleeker, S. E., Moll, H. A., Grobbee, D. E., & Moons, K. G. M. (2003). Internal and external validation of predictive models: A simulation study of bias and precision in small samples. Journal of Clinical Epidemiology, 56(5), 441–447.

    Article  PubMed  Google Scholar 

  25. Dakin, H. (2013). Review of studies mapping from quality of life or clinical measures to EQ-5D: An online database. Health and Quality of Life Outcomes, 11(1), 151.

    Article  PubMed  PubMed Central  Google Scholar 

  26. Wong, S. Q., Fellowes, A., Doig, K., Ellul, J., Bosma, T. J., Irwin, D., et al. (2015). Assessing the clinical value of targeted massively parallel sequencing in a longitudinal, prospective population-based study of cancer patients. British Journal of Cancer, 112(8), 1411–1420.

    Article  CAS  PubMed  Google Scholar 

  27. Osoba, D., Aaronson, N., Zee, B., Sprangers, M., & te Velde, A. (1997). Modification of the EORTC QLQ-C30 (version 2.0) based on content validity and reliability testing in large samples of patients with cancer. The study group on quality of life of the EORTC and the Symptom Control and Quality of Life Committees of the NCI of Canada Clinical Trials Group. Quality of Life Research, 6(2), 103–108.

    Article  CAS  PubMed  Google Scholar 

  28. EORTC. (2001). EORTC QLQ-C30 Scoring Manual (3rd ed.). Brussels: EORTC.

  29. Xie, F., Gaebel, K., Perampaladas, K., Doble, B., & Pullenayegum, E. (2014). Comparing EQ-5D valuation studies: A systematic review and methodological reporting checklist. Medical Decision Making, 34(1), 8–20.

    Article  PubMed  Google Scholar 

  30. Longworth, L., & Rowen, D. (2013). Mapping to obtain EQ-5D utility values for use in NICE health technology assessments. Value in Health : The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 16(1), 202–210.

    Article  Google Scholar 

  31. Matthews, J. N., Altman, D. G., Campbell, M. J., & Royston, P. (1990). Analysis of serial measurements in medical research. BMJ, 300(6719), 230–235.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. San Miguel, J. F., Schlag, R., Khuageva, N. K., Dimopoulos, M. A., Shpilberg, O., Kropff, M., et al. (2008). Bortezomib plus melphalan and prednisone for initial treatment of multiple myeloma. New England Journal of Medicine, 359(9), 906–917.

    Article  CAS  PubMed  Google Scholar 

  33. Huang, I. C., Willke, R. J., Atkinson, M. J., Lenderking, W. R., Frangakis, C., & Wu, A. W. (2007). US and UK versions of the EQ-5D preference weights: Does choice of preference weights make a difference? Quality of Life Research, 16(6), 1065–1072.

    Article  PubMed  Google Scholar 

  34. Pennington, B., & Davis, S. (2014). Mapping from the Health Assessment Questionnaire to the EQ-5D: The impact of different algorithms on cost-effectiveness results. Value in health, 17(8): 762–771.

  35. Chan, K. K., Willan, A. R., Gupta, M., & Pullenayegum, E. (2014). Underestimation of uncertainties in health utilities derived from mapping algorithms involving health-related quality-of-life measures: Statistical explanations and potential remedies. Medical Decision Making, 34(7), 863–872.

    Article  PubMed  Google Scholar 

  36. Fayers, P. M., & Hays, R. D. (2014). Should linking replace regression when mapping from profile-based measures to preference-based measures? Value Health, 17(2), 261–265.

    Article  PubMed  PubMed Central  Google Scholar 

  37. Lu, G., Brazier, J. E., & Ades, A. E. (2013). Mapping from disease-specific to generic health-related quality-of-life scales: A common factor model. Value Health, 16(1), 177–184.

    Article  PubMed  Google Scholar 

  38. Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., & Gray, A. (2015). Preferred reporting items for studies mapping onto preference-based outcome measures: The MAPS statement, Quality of Life Research.

  39. McCabe, C., Edlin, R., Meads, D., Brown, C., & Kharroubi, S. (2013). Constructing indirect utility models: Some observations on the principles and practice of mapping to obtain health state utilities. Pharmacoeconomics, 31(8), 635–641.

    Article  PubMed  Google Scholar 

  40. Xie, F., Pickard, A. S., Krabbe, P. F., Revicki, D., Viney, R., Devlin, N., & Feeny, D. (2015). A checklist for reporting valuation studies of multi-attribute utility-based instruments (CREATE). Pharmacoeconomics, 33(8), 867–877.

    Article  PubMed  PubMed Central  Google Scholar 

  41. Shenfine, J., McNamee, P., Steen, N., Bond, J., & Griffin, S. M. (2005). A pragmatic randomised controlled trial of the cost-effectiveness of palliative therapies for patients with inoperable oesophageal cancer. Health Technology Assessment (Winchester, England), 9(5): 3.

  42. Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35(11), 1095–1108.

    Article  CAS  PubMed  Google Scholar 

  43. Prescott, R. J., Kunkler, I. H., Williams, L. J., King, C. C., Jack, W., van der Pol, M., et al. (2007). A randomised controlled trial of postoperative radiotherapy following breast-conserving surgery in a minimum-risk older population. The PRIME trial. Health Technology Assessment (Winchester, England), 11(31):1–149.

  44. Dolan, P., Gudex, C., Kind, P., & Williams, A. (1996). The time trade-off method: Results from a general population study. Health Economics, 5(2), 141–154.

    Article  CAS  PubMed  Google Scholar 

  45. Shaw, J. W., Johnson, J. A., & Coons, S. J. (2005). US valuation of the EQ-5D health states: Development and testing of the D1 valuation model. Medical Care, 43(3), 203–220.

    Article  PubMed  Google Scholar 

  46. Therasse, P., Mauriac, L., Welnicka-Jaskiewicz, M., Bruning, P., Cufer, T., Bonnefoi, H., et al. (2003). 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. Journal of Clinical Oncology, 21(5), 843–850.

    Article  CAS  PubMed  Google Scholar 

  47. Segeren, C. M., Sonneveld, P., van der Holt, B., Vellenga, E., Croockewit, A. J., Verhoef, G. E., et al. (2003). Overall and event-free survival are not improved by the use of myeloablative therapy following intensified chemotherapy in previously untreated patients with multiple myeloma: A prospective randomized phase 3 study. Blood, 101(6), 2144–2151.

    Article  CAS  PubMed  Google Scholar 

  48. Lamers, L. M., McDonnell, J., Stalmeier, P. F., Krabbe, P. F., & Busschbach, J. J. (2006). The Dutch tariff: Results and arguments for an effective design for national EQ-5D valuation studies. Health Economics, 15(10), 1121–1132.

    Article  CAS  PubMed  Google Scholar 

  49. Doorduijn, J. K., van der Holt, B., van Imhoff, G. W., van der Hem, K. G., Kramer, M. H., van Oers, M. H., et al. (2003). CHOP compared with CHOP plus granulocyte colony-stimulating factor in elderly patients with aggressive non-Hodgkin’s lymphoma. Journal of Clinical Oncology, 21(16), 3041–3050.

    Article  CAS  PubMed  Google Scholar 

  50. Lee, Y. K., Nam, H. S., Chuang, L. H., Kim, K. Y., Yang, H. K., Kwon, I. S., et al. (2009). South Korean time trade-off values for EQ-5D health states: Modeling with observed values for 101 health states. Value in Health : The Journal of the International Society for Pharmacoeconomics and Outcomes Research, 12(8), 1187–1193.

    Article  Google Scholar 

  51. Kim, S. H., Kim, H. J., Lee, S. I., & Jo, M. W. (2012). Comparing the psychometric properties of the EQ-5D-3L and EQ-5D-5L in cancer patients in Korea. Quality of Life Research, 21(6), 1065–1073.

    Article  PubMed  Google Scholar 

  52. Kim, S. H., Hwang, J. S., Kim, T. W., Hong, Y. S., & Jo, M. W. (2012). Validity and reliability of the EQ-5D for cancer patients in Korea. Supportive Care in Cancer, 20(12), 3155–3160.

    Article  PubMed  Google Scholar 

  53. Jordan, K., Proskorovsky, I., Lewis, P., Ishak, J., Payne, K., Lordan, N., et al. (2014). Effect of general symptom level, specific adverse events, treatment patterns, and patient characteristics on health-related quality of life in patients with multiple myeloma: Results of a European, multicenter cohort study. Supportive Care in Cancer, 22(2), 417–426.

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgments

We would like to thank all the cancer patients who participated in the study. We gratefully acknowledge the cooperation of the following Victorian institutions: The Andrew Love Cancer Centre, Geelong Hospital, Barwon Health; The Peter MacCallum Cancer Centre; Ludwig Institute for Cancer Research, Austin Health; Royal Melbourne Hospital, Melbourne Health; Centre for Health Economics, Monash University; Department of Epidemiology and Preventative Medicine, The Alfred Centre, Monash University; Cabrini Health; Department of Pathology, University of Melbourne and Monash Institute of Medical Research. We would also like to thank Mark Lucas and John Parisot for their assistance in organizing the data used in our study.

Financial support information

Brett Doble is supported by research scholarships from Monash University. Paula Lorgelly is a recipient of a Victorian Government Translational Research Grant through the Victorian Cancer Agency. The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, and approval of the manuscript; or decision to submit the manuscript for publication.

Author information

Authors and Affiliations

  1. Centre for Health Economics, Monash Business School, Monash University, 15 Innovation Walk, Clayton, VIC, 3800, Australia

    Brett Doble & Paula Lorgelly

Authors
  1. Brett Doble
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Paula Lorgelly
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Brett Doble.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

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

Additional information

On behalf of the CANCER 2015 Consortium.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 19 kb)

Supplementary material 2 (DOCX 37 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Doble, B., Lorgelly, P. Mapping the EORTC QLQ-C30 onto the EQ-5D-3L: assessing the external validity of existing mapping algorithms. Qual Life Res 25, 891–911 (2016). https://doi.org/10.1007/s11136-015-1116-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-015-1116-2

Keywords

Search

Navigation