Bray, F., Laversanne, M., Sung, H., Ferlay, J., Siegel, R. L., Soerjomataram, I., & Jemal, A. (2024). Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians, 74(3), 229–263. https://doi.org/10.3322/caac.21834
Chen, S., Cao, Z., Prettner, K., Kuhn, M., Yang, J., Jiao, L., Wang, Z., Li, W., Geldsetzer, P., Bärnighausen, T., Bloom, D. E., & Wang, C. (2023). Estimates and projections of the global economic cost of 29 cancers in 204 countries and territories from 2020 to 2050. JAMA Oncology, 9(4), 465–472. https://doi.org/10.1001/jamaoncol.2022.7826
Article PubMed PubMed Central Google Scholar
Maynou, L., & Cairns, J. (2019). What is driving HTA decision-making? Evidence from cancer drug reimbursement decisions from 6 European countries. Health Policy, 123(2), 130–139. https://doi.org/10.1016/j.healthpol.2018.11.003
Bracco, A., & Krol, M. (2013). Economic evaluations in European reimbursement submission guidelines: Current status and comparisons. Expert Review of Pharmacoeconomics & Outcomes Research, 13(5), 579–595. https://doi.org/10.1586/14737167.2013.837766
Mauskopf, J., Walter, J., Birt, J., Bowman, L., Copley-Merriman, C., & Drummond, M. (2011). Differences among formulary submission guidelines: Implications for health technology assessment. International Journal of Technology Assessment in Health Care, 27(3), 261–270. https://doi.org/10.1017/s0266462311000274
National Institute for Health and Care Excellence. (2013). NICE process and methods guides. In Guide to the Methods of Technology Appraisal 2013. National Institute for Health and Care Excellence (NICE).
Copyright © 2013 National Institute for Health and Clinical Excellence, unless otherwise stated. All rights reserved.
Wang, Y., Qiu, T., Zhou, J., Francois, C., & Toumi, M. (2021). Which criteria are considered and how are they evaluated in health technology assessments? A review of methodological guidelines used in Western and Asian countries. Applied Health Economics and Health Policy, 19(3), 281–304. https://doi.org/10.1007/s40258-020-00634-0
Article CAS PubMed Google Scholar
Pan, C. W., He, J. Y., Zhu, Y. B., Zhao, C. H., Luo, N., & Wang, P. (2023). Comparison of EQ-5D-5L and EORTC QLU-C10D utilities in gastric cancer patients. European Journal of Health Economics, 24(6), 885–893. https://doi.org/10.1007/s10198-022-01523-0
Bulamu, N. B., Vissapragada, R., Chen, G., Ratcliffe, J., Mudge, L. A., Smithers, B. M., Isenring, E. A., Smith, L., Jamieson, G. G., & Watson, D. I. (2021). Responsiveness and convergent validity of QLU-C10D and EQ-5D-3L in assessing short-term quality of life following esophagectomy. Health and Quality of Life Outcomes, 19(1), Article 233. https://doi.org/10.1186/s12955-021-01867-w
Article PubMed PubMed Central Google Scholar
Gamper, E. M., Cottone, F., Sommer, K., Norman, R., King, M., Breccia, M., Caocci, G., Patriarca, A., Palumbo, G. A., Stauder, R., Niscola, P., Platzbecker, U., Caers, J., Vignetti, M., & Efficace, F. (2021). The EORTC QLU-C10D was more efficient in detecting clinical known group differences in myelodysplastic syndromes than the EQ-5D-3L. Journal of Clinical Epidemiology, 137, 31–44. https://doi.org/10.1016/j.jclinepi.2021.03.015
Klapproth, C. P., Fischer, F., Rose, M., & Karsten, M. M. (2022). Health state utility differed systematically in breast cancer patients between the EORTC QLU-C10D and the PROMIS preference score. Journal of Clinical Epidemiology, 152, 101–109. https://doi.org/10.1016/j.jclinepi.2022.09.010
Shaw, J. W., Bennett, B., Trigg, A., DeRosa, M., Taylor, F., Kiff, C., Ntais, D., Noon, K., King, M. T., & Cocks, K. (2021). A comparison of generic and condition-specific preference-based measures using data from Nivolumab trials: EQ-5D-3L, mapping to the EQ-5D-5L, and European Organisation for Research and Treatment of Cancer Quality of Life Utility Measure-Core 10 Dimensions. Value in Health, 24(11), 1651–1659. https://doi.org/10.1016/j.jval.2021.05.022
Herdman, M., Kerr, C., Pavesi, M., Garside, J., Lloyd, A., Cubi-Molla, P., & Devlin, N. (2020). Testing the validity and responsiveness of a new cancer-specific health utility measure (FACT-8D) in relapsed/refractory mantle cell lymphoma, and comparison to EQ-5D-5L. Journal of Patient-Reported Outcomes, 4(1), Article 22. https://doi.org/10.1186/s41687-020-0185-3
Article PubMed PubMed Central Google Scholar
Kularatna, S., Whitty, J. A., Johnson, N. W., Jayasinghe, R., & Scuffham, P. A. (2016). A comparison of health state utility values associated with oral potentially malignant disorders and oral cancer in Sri Lanka assessed using the EQ-5D-3 L and the EORTC-8D. Health and Quality of Life Outcomes, 14, Article 101. https://doi.org/10.1186/s12955-016-0502-y
Article PubMed PubMed Central Google Scholar
Zhang, A., Mao, Z., Wang, Z., Wu, J., Luo, N., & Wang, P. (2023). Comparing measurement properties of EQ-5D and SF-6D in East and South-East Asian populations: A scoping review. Expert Review of Pharmacoeconomics & Outcomes Research, 23(5), 449–468. https://doi.org/10.1080/14737167.2023.2189590
Whitehurst, D. G., Bryan, S., & Lewis, M. (2011). Systematic review and empirical comparison of contemporaneous EQ-5D and SF-6D group mean scores. Medical Decision Making, 31(6), E34-44. https://doi.org/10.1177/0272989x11421529
Rencz, F., Gulácsi, L., Drummond, M., Golicki, D., & Péntek, M. (2016). EQ-5D in Central and Eastern Europe: 2000–2015. Quality of Life Research, 25(11), 1–18.
Chun-Lin, J., Hai-Yin, W., & Jie, C. (2014). Methods, applications and recommendations for health technology assessment. Chinese Health Resources.
Garau, M., Shah, K. K., Mason, A. R., Wang, Q., Towse, A., & Drummond, M. F. (2011). Using QALYs in cancer: A review of the methodological limitations. PharmacoEconomics, 29, 673–685.
Janssen, M. F., Pickard, A. S., Golicki, D., Gudex, C., Niewada, M., Scalone, L., Swinburn, P., & Busschbach, J. (2013). Measurement properties of the EQ-5D-5L compared to the EQ-5D-3L across eight patient groups: A multi-country study. Quality of Life Research, 22(7), 1717–1727. https://doi.org/10.1007/s11136-012-0322-4
Article CAS PubMed Google Scholar
Chen, G., & Olsen, J. A. (2020). Filling the psycho-social gap in the EQ-5D: The empirical support for four bolt-on dimensions. Quality of Life Research, 29(11), 3119–3129. https://doi.org/10.1007/s11136-020-02576-5
Article PubMed PubMed Central Google Scholar
Marriott, E. R., van Hazel, G., Gibbs, P., & Hatswell, A. J. (2017). Mapping EORTC-QLQ-C30 to EQ-5D-3L in patients with colorectal cancer. Journal of Medical Economics, 20(2), 193–199. https://doi.org/10.1080/13696998.2016.1241788
King, M. T., Costa, D. S., Aaronson, N. K., Brazier, J. E., Cella, D. F., Fayers, P. M., Grimison, P., Janda, M., Kemmler, G., Norman, R., Pickard, A. S., Rowen, D., Velikova, G., Young, T. A., & Viney, R. (2016). QLU-C10D: A health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30. Quality of Life Research, 25(3), 625–636. https://doi.org/10.1007/s11136-015-1217-y
Article CAS PubMed Google Scholar
McTaggart-Cowan, H., King, M. T., Norman, R., Costa, D. S. J., Pickard, A. S., Viney, R., & Peacock, S. J. (2022). The FACT-8D, a new cancer-specific utility algorithm based on the Functional Assessment of Cancer Therapies-General (FACT-G): A Canadian valuation study. Health and Quality of Life Outcomes, 20(1), Article 97. https://doi.org/10.1186/s12955-022-02002-z
Article PubMed PubMed Central Google Scholar
Xu, R. H., Zhao, Z., Pan, T., Monteiro, A., Gu, H., & Dong, D. (2025). Comparing the measurement properties of the EQ-5D-5 L, SF-6Dv2, QLU-C10D and FACT-8D among survivors of classical Hodgkin’s lymphoma. European Journal of Health Economics, 26(4), 671–682. https://doi.org/10.1007/s10198-024-01730-x
Rabin, R., & de Charro, F. (2001). EQ-5D: A measure of health status from the EuroQol Group. Annals of Medicine, 33(5), 337–343. https://doi.org/10.3109/07853890109002087
Article CAS PubMed Google Scholar
Luo, N., Liu, G., Li, M., Guan, H., Jin, X., & Rand-Hendriksen, K. (2017). Estimating an EQ-5D-5L value set for China. Value in Health, 20(4), 662–669. https://doi.org/10.1016/j.jval.2016.11.016
Brazier, J. E., Mulhern, B. J., Bjorner, J. B., Gandek, B., Rowen, D., Alonso, J., Vilagut, G., & Ware, J. E. (2020). Developing a new version of the SF-6D health state classification system from the SF-36v2: SF-6Dv2. Medical Care, 58(6), 557–565. https://doi.org/10.1097/mlr.0000000000001325
Wu, J., Xie, S., He, X., Chen, G., Bai, G., Feng, D., Hu, M., Jiang, J., Wang, X., Wu, H., Wu, Q., & Brazier, J. E. (2021). Valuation of SF-6Dv2 health states in China using time trade-off and discrete-choice experiment with a duration dimension. PharmacoEconomics, 39(5), 521–535. https://doi.org/10.1007/s40273-020-00997-1
Article PubMed PubMed Central Google Scholar
Norman, R., Viney, R., Aaronson, N. K., Brazier, J. E., Cella, D., Costa, D. S., Fayers, P. M., Kemmler, G., Peacock, S., Pickard, A. S., Rowen, D., Street, D. J., Velikova, G., Young, T. A., & King, M. T. (2016). Using a discrete choice experiment to value the QLU-C10D: Feasibility and sensitivity to presentation format. Quality of Life Research, 25(3), 637–649. https://doi.org/10.1007/s11136-015-1115-3
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