Mapping the cancer-specific EORTC QLQ-BR53 onto the preference-based EQ-5D-5L instrument: A cross-sectional study in breast cancer in Vietnam

To Hue Nghi; Vo Ngoc Yen Nhi; Le Tuan Anh; Tran Nguyen Ha; Pham Thi Cam Phuong; Nguyen Thi Thu Thuy

doi:10.59294/HIUJS.VOL.7.2024.687

Tóm tắt

Introduction: The EQ-5D instrument is highly recommended for health economic evaluations but is considered less practical than the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC) QLQ-BR53 in clinical studies with cancer patients. In these cases, cross-walking is recommended to convert the cancer-specific instrument onto the preference-based measures. This study aimed to develop an algorithm for direct mapping the QLQ-BR53 onto the EQ-5D-5L utility index in breast cancer patients. Methods: A cross-sectional study was conducted among patients diagnosed with HER2 breast cancer across six oncology hospitals in Vietnam from July to December 2020. Participants' health-related quality of life was assessed using QLQ-BR53 and EQ-5D. Two mapping algorithms - ordinary least squares (OLS) and generalized linear regression (GLM) - were compared. The best-fit model was selected based on MAE, RMSE, MAPE, and AIC. Internal validation was done using hold-out and cross-validation methods. Results: The study involved 338 participants with a mean age of 53.87 ± 9.97 years. Most were diagnosed early (55.7%) and non-metastatic (76.6%). The mean EQ-5D utility value was 0.863 ± 0.142. The OLS model was the best fit for mapping EQ-5D utility scores from QLQ-BR53, with goodness-of-fit statistics: MAE = 0.786; RMSE = 0.1038; MAPE = 11.68%; and AIC = -524.2398. Key components included global health status, future perspective, pain, and arm symptoms. Conclusion: The developed model allows mapping QLQ-BR53 breast cancer data to EQ-5D-5L utility values, aiding in calculating quality-adjusted life years (QALYs) for cost-utility analyses in breast cancer.

Abstract

Introduction: The EQ-5D instrument is highly recommended for health economic evaluations but is considered less practical than the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC) QLQ-BR53 in clinical studies with cancer patients. In these cases, cross-walking is recommended to convert the cancer-specific instrument onto the preference-based measures. This study aimed to develop an algorithm for direct mapping the QLQ-BR53 onto the EQ-5D-5L utility index in breast cancer patients. Methods: A cross-sectional study was conducted among patients diagnosed with HER2 breast cancer across six oncology hospitals in Vietnam from July to December 2020. Participants' health-related quality of life was assessed using QLQ-BR53 and EQ-5D. Two mapping algorithms - ordinary least squares (OLS) and generalized linear regression (GLM) - were compared. The best-fit model was selected based on MAE, RMSE, MAPE, and AIC. Internal validation was done using hold-out and cross-validation methods. Results: The study involved 338 participants with a mean age of 53.87 ± 9.97 years. Most were diagnosed early (55.7%) and non-metastatic (76.6%). The mean EQ-5D utility value was 0.863 ± 0.142. The OLS model was the best fit for mapping EQ-5D utility scores from QLQ-BR53, with goodness-of-fit statistics: MAE = 0.786; RMSE = 0.1038; MAPE = 11.68%; and AIC = -524.2398. Key components included global health status, future perspective, pain, and arm symptoms. Conclusion: The developed model allows mapping QLQ-BR53 breast cancer data to EQ-5D-5L utility values, aiding in calculating quality-adjusted life years (QALYs) for cost-utility analyses in breast cancer.

Tài liệu tham khảo

[1] C. f. D. C. a. Prevention. (10/10/2021). What is breast cancer? Available: https://www.cdc.gov/cancer/breast/basic_info/what-is-breast-cancer.htm

[2] Z. Tao, A. Shi, C. Lu, T. Song, Z. Zhang, and J. Zhao, "Breast cancer: Epidemiology and Etiology," (in eng), Cell Biochem Biophys, vol. 72, no. 2, pp. 333-8, Jun 2015.

DOI: https://doi.org/10.1007/s12013-014-0459-6

[3] H. Sung et al., "Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and mortality worldwide for 36 cancers in 185 countries," (in eng), CA Cancer J Clin, vol. 71, no. 3, pp. 209-249, May 2021.

DOI: https://doi.org/10.3322/caac.21660

[4] W. H. Organiztion. (2020, 22/3/2021). Breast cancer statistics in Vietnam. Available: https://gco.iarc.fr/today/data/factsheets/populations/704-viet-nam-fact-sheets.pdf

[5] R. Kazi, S. Sayed, and R. C. Dwivedi, "Clinical importance of quality of life measures in head and neck cancer," (in eng), Indian J Cancer, vol. 47, no. 3, pp. 237-8, Jul-Sep 2010.

DOI: https://doi.org/10.4103/0019-509X.64705

[6] H. National Institute for and E. Care, "NICE Process and Methods Guides," in Guide to the methods of technology appraisal 2013 London: National Institute for Health and Care Excellence (NICE) Copyright © 2013 National Institute for Health and Clinical Excellence, unless otherwise stated. All rights reserved., 2013.

[7] P. J. Ho, S. A. M. Gernaat, M. Hartman, and H. M. Verkooijen, "Health-related quality of life in Asian patients with breast cancer: a systematic review," (in eng), BMJ Open, vol. 8, no. 4, p. e020512, Apr 20 2018.

DOI: https://doi.org/10.1136/bmjopen-2017-020512

[8] K. Matter-Walstra, D. Klingbiel, T. Szucs, B. C. Pestalozzi, and M. Schwenkglenks, "Using the EuroQol EQ-5D in Swiss cancer patients, which value set should be applied?," (in eng), Pharmacoeconomics, vol. 32, no. 6, pp. 591-9, Jun 2014.

DOI: https://doi.org/10.1007/s40273-014-0151-0

[9] N. K. Aaronson 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," (in eng), J Natl Cancer Inst, vol. 85, no. 5, pp. 365-76, Mar 3 1993.

DOI: https://doi.org/10.1093/jnci/85.5.365

[10] H. Dakin, L. Abel, R. Burns, and Y. Yang, "Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: An online database and application of the MAPS statement," (in eng), Health Qual Life Outcomes, vol. 16, no. 1, p. 31, Feb 12 2018.

DOI: https://doi.org/10.1186/s12955-018-0857-3

[11] J. E. Brazier, Y. Yang, A. Tsuchiya, and D. L. Rowen, "A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures," (in eng), Eur J Health Econ, vol. 11, no. 2, pp. 215-25, Apr 2010.

DOI: https://doi.org/10.1007/s10198-009-0168-z

[12] D. Mortimer and L. Segal, "Comparing the incomparable? A systematic review of competing techniques for converting descriptive measures of health status into QALY-weights," (in eng), Med Decis Making, vol. 28, no. 1, pp. 66-89, Jan-Feb 2008.

DOI: https://doi.org/10.1177/0272989X07309642

[13] H. Dakin, "Review of studies mapping from quality of life or clinical measures to EQ-5D: an online database," Health and Quality of Life Outcomes, vol. 11, no. 1, p. 151, 2013/09/05 2013.

DOI: https://doi.org/10.1186/1477-7525-11-151

[14] C. Mukuria et al., "An updated systematic review of studies Mapping (or cross-walking) measures of health-related quality of life to generic preference-based measures to generate Utility values," (in eng), Appl Health Econ Health Policy, vol. 17, no. 3, pp. 295-313, Jun 2019.

DOI: https://doi.org/10.1007/s40258-019-00467-6

[15] T. Liu, S. Li, M. Wang, Q. Sun, and G. Chen, "Mapping the Chinese Version of the EORTC QLQ- BR53 Onto the EQ-5D-5L and SF-6D Utility Scores," (in eng), Patient, vol. 13, no. 5, pp. 537-555, Oct 2020.

DOI: https://doi.org/10.1007/s40271-020-00422-x