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Mapping EORTC-QLQ-C30 and QLQ-CR29 onto EQ-5D-5L in Colorectal Cancer Patients

  • Hosein Ameri
  • Mahmood Yousefi
  • Mehdi Yaseri
  • Azin Nahvijou
  • Mohammad Arab
  • Ali Akbari SariEmail author
Original Research
  • 30 Downloads

Abstract

Purpose

Patient-level utility data are needed for cost-utility analysis; in oncology, however, the data are commonly gathered using disease-specific questionnaires that are often not appropriate. Present study aimed to derive an algorithm which can map the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire-C30 (EORTC QLQ-C30) scales and the Colorectal Cancer-Specific Quality Of Life Questionnaire (QLQ-CR29) scales onto the EuroQoL 5-Dimension 5-Level (EQ-5D-5L) values in patients with colorectal cancer (CRC).

Methods

Using the Ordinary Least Square (OLS) model, a cross-sectional dataset of 252 patients with CRC were gathered from three academic centers of cancer treatment in Tehran in 2017. The predicted R2 (Pred R2) and adjusted R2 (Adj R2) are used to evaluate model goodness of fit. Additionally, mean absolute error (MAE), root mean square error (RMSE), Spearman’s correlation coefficients (ρ), and intraclass correlation (ICC) are applied to assess predictive ability of models. The tenfold cross-validation procedure was applied for validation models.

Results

According to the results of our study, the model C4 from EORTC QLQ-C30 was the best predictive model (Pred R2 = 66.57%, Adj R2 = 67.67%, RMSE = 0.10173, MAE = 0.07840). Also, the model R4 from QLQ-CR29 performed the best for EQ-5D-5L (Adj R2 = 48.42%, Pred R2 = 45.54%, MAE = 0.10051, RMSE = 0.12997).

Conclusions

The mapping algorithm successfully mapped the EORTC QLQ-C30 and QLQ-CR29 scales onto the EQ-5D-5L values; therefore, it enables policymakers to convert cancer-specific questionnaires scores to the preference-based scores.

Keywords

EORTC QLQ-C30 QLQ-CR29 EQ-5D-5L Mapping Colorectal cancer Quality of life 

Notes

Acknowledgments

The authors hereby bestow much gratitude to the chemotherapy and radiography departments of Imam Khomeini, Shohadaye Tajrish, and Shohadaye Haft Tir Hospitals for their valuable collaboration and participation in the present study. We also express our gratitude to EuroQoL Group who converted EQ-5D-3L into EQ-5D-5L values. This report was approved by the ethics committee of the three selected centers.

Funding

This study was funded by Iranian National Science Foundation (grant number 96010306), and Tehran University of Medical Sciences (grant number 240/1938).

Compliance with Ethical Standards

Ethical Information

This report is part of a PhD project that was and was approved by the ethics committee of the three selected centers, registration number: 240/1938.

Informed Consent

Informed consent was obtained from all patients included in the study.

Conflict of Interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Ferlay J, Soerjomataram I, Dikshit R, Eser S, Mathers C, Rebelo M, et al. Cancer incidence and mortality worldwide: sources, methods and major patterns in GLOBOCAN 2012. Int J Cancer. 2015;136(5):E359–E86.CrossRefGoogle Scholar
  2. 2.
    Marriott E-R, van Hazel G, Gibbs P, Hatswell AJ. Mapping EORTC-QLQ-C30 to EQ-5D-3L in patients with colorectal cancer. J Med Econ. 2017;20(2):193–9.CrossRefGoogle Scholar
  3. 3.
    Aaronson NK, Ahmedzai S, Bergman B, Bullinger M, Cull A, Duez NJ, et al. The European Organization for Research and Treatment of Cancer QLQ-C30: a quality-of-life instrument for use in international clinical trials in oncology. J Natl Cancer Inst. 1993;85(5):365–76.CrossRefGoogle Scholar
  4. 4.
    Arnold D, Girling A, Stevens A, Lilford R. Comparison of direct and indirect methods of estimating health state utilities for resource allocation: review and empirical analysis. Bmj. 2009;339:b2688.CrossRefGoogle Scholar
  5. 5.
    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. 2016;25(4):891–911.CrossRefGoogle Scholar
  6. 6.
    Kim E-J, Ko S-K, Kang H-Y. Mapping the cancer-specific EORTC QLQ-C30 and EORTC QLQ-BR23 to the generic EQ-5D in metastatic breast cancer patients. Qual Life Res. 2012;21(7):1193–203.CrossRefGoogle Scholar
  7. 7.
    Kim SH, Jo M-W, Kim H-J, Ahn J-H. Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients. Health Qual Life Outcomes. 2012;10(1):151.CrossRefGoogle Scholar
  8. 8.
    Kontodimopoulos N, Aletras VH, Paliouras D, Niakas D. Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Value Health. 2009;12(8):1151–7.CrossRefGoogle Scholar
  9. 9.
    McKenzie L, Van Der Pol M. Mapping the EORTC QLQ C-30 onto the EQ-5D instrument: the potential to estimate QALYs without generic preference data. Value Health. 2009;12(1):167–71.CrossRefGoogle Scholar
  10. 10.
    Proskorovsky I, Lewis P, Williams CD, Jordan K, Kyriakou C, Ishak J, et al. Mapping EORTC QLQ-C30 and QLQ-MY20 to EQ-5D in patients with multiple myeloma. Health Qual Life Outcomes. 2014;12(1):35.CrossRefGoogle Scholar
  11. 11.
    Crott R, Briggs A. Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences. Eur J Health Econ. 2010;11(4):427–34.CrossRefGoogle Scholar
  12. 12.
    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(1):151.CrossRefGoogle Scholar
  13. 13.
    Jang RW, Isogai PK, Mittmann N, Bradbury PA, Shepherd FA, Feld R, et al. Derivation of utility values from European Organization for Research and Treatment of Cancer Quality of Life-Core 30 questionnaire values in lung cancer. J Thorac Oncol. 2010;5(12):1953–7.CrossRefGoogle Scholar
  14. 14.
    Versteegh MM, Leunis A, Luime JJ, Boggild M, Uyl-de Groot CA, Stolk EA. Mapping Qlq-C30, Haq, and Msis-29 on Eq-5d. Med Decis Mak. 2012;32(4):554–68.CrossRefGoogle Scholar
  15. 15.
    Wang P, Luo N, Tai E, Thumboo J. The EQ-5D-5L is more discriminative than the EQ-5D-3L in patients with diabetes in Singapore. Value Health Reg Issues. 2016;9:57–62.CrossRefGoogle Scholar
  16. 16.
    Agborsangaya CB, Lahtinen M, Cooke T, Johnson JA. Comparing the EQ-5D 3L and 5L: measurement properties and association with chronic conditions and multimorbidity in the general population. Health Qual Life Outcomes. 2014;12(1):74.CrossRefGoogle Scholar
  17. 17.
    Antunes P, Ferreira LN, Ferreira PL, Pereira LN, editors. Comparing the EQ-5D-3L and 5L versions in a sample of young Portuguese adults. Quality of life research. Dordrecht: Springer; 2016.Google Scholar
  18. 18.
    Wong CKH, Lam CL, Wan Y, Rowen D. Predicting SF-6D from the European organization for treatment and research of cancer quality of life questionnaire scores in patients with colorectal cancer. Value Health. 2013;16(2):373–84.CrossRefGoogle Scholar
  19. 19.
    Van Hout B, Janssen M, Feng Y-S, Kohlmann T, Busschbach J, Golicki D, et al. Interim scoring for the EQ-5D-5L: mapping the EQ-5D-5L to EQ-5D-3L value sets. Value Health. 2012;15(5):708–15.CrossRefGoogle Scholar
  20. 20.
    QLQ E. C30 Reference values. 0. Quality of Life Departement, EORTC Headquaters, Brüssel, Belgien. 2008.Google Scholar
  21. 21.
    Whistance R, Conroy T, Chie W, Costantini A, Sezer O, Koller M, et al. Clinical and psychometric validation of the EORTC QLQ-CR29 questionnaire module to assess health-related quality of life in patients with colorectal cancer. Eur J Cancer. 2009;45(17):3017–26.CrossRefGoogle Scholar
  22. 22.
    Khazaeli N, Golshiri P, Farajzadegan Z, Hemati S, Amouheidari A, Hakimian MR, et al. Evaluating the validity and reliability of Persian version of the European Organization for Research and Treatment of Cancer Quality of Life questionnaire for colorectal Cancer (EORTC QLQ-CR29). Journal of Isfahan Medical School. 2014;32(276).Google Scholar
  23. 23.
    Montazeri A, Harirchi I, Vahdani M, Khaleghi F, Jarvandi S, Ebrahimi M, et al. The European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30): translation and validation study of the Iranian version. Support Care Cancer. 1999;7(6):400–6.CrossRefGoogle Scholar
  24. 24.
    Rabin R, Oemar M, Oppe M, Janssen B, Herdman M. EQ-5D-3L user guide. Basic information on how to use the EQ-5D-5L instrument Rotterdam: EuroQol group, vol. 22; 2011.Google Scholar
  25. 25.
    Goudarzi R, Zeraati H, Sari AA, Rashidian A, Mohammad K. Population-based preference weights for the EQ-5D health states using the visual analogue scale (VAS) in Iran. Iran Red Crescent Med J. 2016;18(2).Google Scholar
  26. 26.
    Ameri H, Yousefi M, Yaseri M, Nahvijou A, Arab M, Akbari Sari A. Mapping the cancer-specific QLQ-C30 onto the generic EQ-5D-5L and SF-6D in colorectal cancer patients. Expert Rev Pharmacoecon Outcomes Res. 2018:1–8.Google Scholar
  27. 27.
    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.CrossRefGoogle Scholar
  28. 28.
    Wong CK, Lam CL, Rowen D, McGhee SM, Ma K-P, Law W-L, 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.CrossRefGoogle Scholar
  29. 29.
    Yang Y, Wong MY, Lam CL, Wong CK. Improving the mapping of condition-specific health-related quality of life onto SF-6D score. Qual Life Res. 2014;23(8):2343–53.CrossRefGoogle Scholar
  30. 30.
    Grieve R, Grishchenko M, Cairns J. SF-6D versus EQ-5D: reasons for differences in utility scores and impact on reported cost-utility. Eur J Health Econ. 2009;10(1):15–23.CrossRefGoogle Scholar
  31. 31.
    van Reenen M, Janssen B. EQ-5D-5L user guide: basic information on how to use the EQ-5D-5L instrument. Rotterdam: EuroQol Research Foundation; 2015.Google Scholar
  32. 32.
    Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ. 2002;21(2):271–92.CrossRefGoogle Scholar
  33. 33.
    Ara R, Brazier J. Deriving an algorithm to convert the eight mean SF-36 dimension scores into a mean EQ-5D preference-based score from published studies (where patient level data are not available). Value Health. 2008;11(7):1131–43.CrossRefGoogle Scholar
  34. 34.
    Cheung Y, Tan L, Lau P, Au W, Luo N. Mapping the eight-item Parkinson’s Disease Questionnaire (PDQ-8) to the EQ-5D utility index. Qual Life Res. 2008;17(9):1173–81.CrossRefGoogle Scholar
  35. 35.
    Devlin NJ, Krabbe PF. The development of new research methods for the valuation of EQ-5D-5L. Berlin: Springer; 2013.CrossRefGoogle Scholar
  36. 36.
    Purba FD, Hunfeld JA, Iskandarsyah A, Fitriana TS, Sadarjoen SS, Ramos-Goñi JM, et al. The Indonesian EQ-5D-5L value set. Pharmacoeconomics. 2017;35(11):1153–65.CrossRefGoogle Scholar
  37. 37.
    Kim S-H, Ahn J, Ock M, Shin S, Park J, Luo N, et al. The EQ-5D-5L valuation study in Korea. Qual Life Res. 2016;25(7):1845–52.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Hosein Ameri
    • 1
  • Mahmood Yousefi
    • 2
  • Mehdi Yaseri
    • 3
  • Azin Nahvijou
    • 1
    • 4
  • Mohammad Arab
    • 1
  • Ali Akbari Sari
    • 1
    Email author
  1. 1.Department of Health Management and Economics, School of Public HealthTehran University of Medical SciencesTehranIran
  2. 2.Iranian Center of Excellence in Health Management, School of Management and Medical Informatics, Health Economics DepartmentTabriz University of Medical SciencesTabrizIran
  3. 3.Department of Epidemiology and Biostatistics, School of Public HealthTehran University of Medical SciencesTehranIran
  4. 4.Cancer Research Center, Cancer InstituteTehran University of Medical SciencesTehranIran

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