A methodological study of health utility measurement based on discrete choice experiment_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Xiaolu ¹ , LI Rui ¹ , GAO Xin ¹ , XIAO Feiyi ¹ , LUO Chuanchao ² , PENG Xin ² ,  GUO Wudong ¹ ,  LI Xue ¹

1. China National Health Development Research Center, Beijing 100191, P. R. China;
2. Institute of Pharmaceutical Economics, School of Pharmacy, Sun Yat sen University, Guangzhou 510006, P. R. China;

Corresponding author：

GUO Wudong, Email: guowudong@hotmail.com; LI Xue, Email: lx0204@126.co

Keywords：

Discrete choice experiment (DCE); Utility; Preference; Health-related quality of life (HRQOL)

DOI：

10.7507/1672-2531.202405008

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The discrete choice experiment (DCE) is a stated preference analysis method used to evaluate the impact of multiple factors on individual choice, which has been explored by scholars around the world for health utility measurement. This method is considered to reduce the cognitive burden of traditional utility measurement methods and has high development potential. By examining empirical studies conducted domestically and internationally that employ DCE for measuring health utility, and drawing on methodological guidelines for constructing DCE models, this paper provides an overview of the methodological background of DCE, the practical process used for measuring health utility, and discusses relevant challenges in its application.

1.	Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA, 1995, 273(1): 59-65.
2.	De Vet HC, Terwee CB, Mokkink LB, et al. Measurement in medicine: a practical guide. London: Cambridge university press, 2011.
3.	孙利华, 吴晶. 药物经济学. 北京: 人民卫生出版社, 2022.
4.	中国药物经济学评价指南2020: T/CPHARMA 003-2020. 2020.
5.	Blinman P, King M, Norman R, et al. Preferences for cancer treatments: an overview of methods and applications in oncology. Ann Oncol, 2012, 23(5): 1104-1110.
6.	Drummond MF, Sculpher MJ, Claxton K, et al. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press, 2015.
7.	King MT, Viney R, Simon Pickard A, et al. Australian utility weights for the EORTC QLU-C10D, a multi-attribute utility instrument derived from the cancer-specific quality of life questionnaire, EORTC QLQ-C30. Pharmacoeconomics, 2018, 36(2): 225-238.
8.	Norman R, Cronin P, Viney R. A pilot discrete choice experiment to explore preferences for EQ-5D-5L health states. Appl Health Econ Health Policy, 2013, 11(3): 287-298.
9.	Norman R, Viney R, Brazier J, et al. Valuing SF-6D health states using a discrete choice experiment. Med Decis Making, 2014, 34(6): 773-786.
10.	King MT, Norman R, Mercieca-Bebber R, et al. The functional assessment of cancer therapy eight dimension (FACT-8D), a multi-attribute utility instrument derived from the cancer-specific FACT-general (FACT-G) quality of life questionnaire: development and Australian value set. Value Health, 2021, 24(6): 862-873.
11.	Norman R, Viney R, Aaronson NK, et al. Using a discrete choice experiment to value the QLU-C10D: feasibility and sensitivity to presentation format. Qual Life Res, 2016, 25(3): 637-649.
12.	Wang Y, Wang Z, Wang Z, et al. Application of discrete choice experiment in health care: a bibliometric analysis. Front Public Health, 2021, 9: 673698.
13.	Louviere JJ, Lancsar E. Choice experiments in health: the good, the bad, the ugly and toward a brighter future. Health Econ Policy Law, 2009, 4(Pt 4): 527-546.
14.	Louviere JJ, Hensher DA, Swait JD. Stated choice methods: analysis and applications. London: Cambridge university press, 2000.
15.	Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ, 2000, 320(7248): 1530-1533.
16.	包美玲, 黄存瑞, 王皓翔. 离散选择实验在卫生人力资源领域的应用进展. 中国全科医学, 2024, 27(25): 3184-3191.
17.	Soekhai V, de Bekker-Grob EW, Ellis AR, et al. Discrete choice experiments in health economics: past, present and future. Pharmacoeconomics, 2019, 37(2): 201-226.
18.	Fields BE, Bell JF, Bigbee JL, et al. Registered nurses' preferences for rural and urban jobs: a discrete choice experiment. Int J Nurs Stud, 2018, 86: 11-19.
19.	World Health Organization. How to conduct a discrete choice experiment for health workforce recruitment and retention in remote and rural areas: a user guide with case studies. 2012.
20.	Szinay D, Cameron R, Naughton F, et al. Understanding uptake of digital health products: methodology tutorial for a discrete choice experiment using the Bayesian efficient design. J Med Internet Res, 2021, 23(10): e32365.
21.	张春丽, 邱佳玲, 陈莎, 等. 离散选择实验潜类别logit模型在卫生服务领域的应用与Stata软件的实现. 中国卫生统计, 2023, 40(4): 502-506.
22.	Cohen SE, Todd PM. Stated and revealed preferences in companion animal choice. Behav Res Methods, 2019, 51(4): 1498-1509.
23.	De Bekker-Grob E. Discrete choice experiments in health care: theory and applications. 2009.
24.	Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user's guide. Pharmacoeconomics, 2008, 26(8): 661-677.
25.	Cascetta E. Transportation systems analysis: models and applications. Boston MA: Springer US, 2009.
26.	King MT, Costa DS, Aaronson NK, et al. QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30. Qual Life Res, 2016, 25(3): 625-636.
27.	谢诗桐. 基于方法对比的中国一般人群健康效用积分体系构建研究. 天津: 天津大学, 2020.
28.	Bahrampour M, Byrnes J, Norman R, et al. Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods. Eur J Health Econ, 2020, 21(7): 983-992.
29.	Bhattacherjee A. Social science research: principles, methods and practices (revised edition). 2019.
30.	Zhu W, Zhang M, Pan J, et al. Valuing Chinese medicine quality of life-11 dimensions (CQ-11D) health states using a discrete choice experiment with survival duration (DCETTO). Health Qual Life Outcomes, 2023, 21(1): 99.
31.	Sawtooth Software. Sample size issues for conjoint analysis studies. 1998.
32.	de Bekker-Grob EW, Donkers B, Jonker MF, et al. Sample size requirements for discrete-choice experiments in healthcare: a practical guide. Patient, 2015, 8(5): 373-384.
33.	Demirkale F, Donovan D, Street DJ. Constructing D-optimal symmetric stated preference discrete choice experiments. J Stat Plan Infer, 2013, 143(8): 1380-1391.
34.	Alamri AS, Georgiou S, Stylianou S. Discrete choice experiments: an overview on constructing D-optimal and near-optimal choice sets. Heliyon, 2023, 9(7): e18256.
35.	伍红艳, 孙利华, 任晓晓, 等. 疾病特异性生命质量量表效用值积分体系的构建方法及探讨. 中国卫生经济, 2012, 31(3): 7-10.
36.	Robinson A, Spencer A, Moffatt P. A framework for estimating health state utility values within a discrete choice experiment: modeling risky choices. Med Decis Making, 2015, 35(3): 341-350.
37.	Sutton AJ, Abrams KR. Bayesian methods in meta-analysis and evidence synthesis. Stat Methods Med Res, 2001, 10(4): 277-303.
38.	Higgins JP, Green S. Cochrane handbook for systematic reviews of interventions. 2008.
39.	Vass C, Rigby D, Campbell S, et al. Investigating the framing of risk attributes in a discrete choice experiment: an application of eye-tracking and think aloud. The 36th meeting of the Society for Medical Decision Making, 2014.
40.	Krucien N, Ryan M, Hermens F. Using eye-tracking methods to inform decision making processes in discrete choice experiments, Health Economists’ Study Group (HESG). 2014.
41.	国家统计局. 中国统计年鉴2023年. 2024.

1. Wilson IB, Cleary PD. Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes. JAMA, 1995, 273(1): 59-65.
2. De Vet HC, Terwee CB, Mokkink LB, et al. Measurement in medicine: a practical guide. London: Cambridge university press, 2011.
3. 孙利华, 吴晶. 药物经济学. 北京: 人民卫生出版社, 2022.
4. 中国药物经济学评价指南2020: T/CPHARMA 003-2020. 2020.
5. Blinman P, King M, Norman R, et al. Preferences for cancer treatments: an overview of methods and applications in oncology. Ann Oncol, 2012, 23(5): 1104-1110.
6. Drummond MF, Sculpher MJ, Claxton K, et al. Methods for the economic evaluation of health care programmes. Oxford: Oxford University Press, 2015.
7. King MT, Viney R, Simon Pickard A, et al. Australian utility weights for the EORTC QLU-C10D, a multi-attribute utility instrument derived from the cancer-specific quality of life questionnaire, EORTC QLQ-C30. Pharmacoeconomics, 2018, 36(2): 225-238.
8. Norman R, Cronin P, Viney R. A pilot discrete choice experiment to explore preferences for EQ-5D-5L health states. Appl Health Econ Health Policy, 2013, 11(3): 287-298.
9. Norman R, Viney R, Brazier J, et al. Valuing SF-6D health states using a discrete choice experiment. Med Decis Making, 2014, 34(6): 773-786.
10. King MT, Norman R, Mercieca-Bebber R, et al. The functional assessment of cancer therapy eight dimension (FACT-8D), a multi-attribute utility instrument derived from the cancer-specific FACT-general (FACT-G) quality of life questionnaire: development and Australian value set. Value Health, 2021, 24(6): 862-873.
11. Norman R, Viney R, Aaronson NK, et al. Using a discrete choice experiment to value the QLU-C10D: feasibility and sensitivity to presentation format. Qual Life Res, 2016, 25(3): 637-649.
12. Wang Y, Wang Z, Wang Z, et al. Application of discrete choice experiment in health care: a bibliometric analysis. Front Public Health, 2021, 9: 673698.
13. Louviere JJ, Lancsar E. Choice experiments in health: the good, the bad, the ugly and toward a brighter future. Health Econ Policy Law, 2009, 4(Pt 4): 527-546.
14. Louviere JJ, Hensher DA, Swait JD. Stated choice methods: analysis and applications. London: Cambridge university press, 2000.
15. Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ, 2000, 320(7248): 1530-1533.
16. 包美玲, 黄存瑞, 王皓翔. 离散选择实验在卫生人力资源领域的应用进展. 中国全科医学, 2024, 27(25): 3184-3191.
17. Soekhai V, de Bekker-Grob EW, Ellis AR, et al. Discrete choice experiments in health economics: past, present and future. Pharmacoeconomics, 2019, 37(2): 201-226.
18. Fields BE, Bell JF, Bigbee JL, et al. Registered nurses' preferences for rural and urban jobs: a discrete choice experiment. Int J Nurs Stud, 2018, 86: 11-19.
19. World Health Organization. How to conduct a discrete choice experiment for health workforce recruitment and retention in remote and rural areas: a user guide with case studies. 2012.
20. Szinay D, Cameron R, Naughton F, et al. Understanding uptake of digital health products: methodology tutorial for a discrete choice experiment using the Bayesian efficient design. J Med Internet Res, 2021, 23(10): e32365.
21. 张春丽, 邱佳玲, 陈莎, 等. 离散选择实验潜类别logit模型在卫生服务领域的应用与Stata软件的实现. 中国卫生统计, 2023, 40(4): 502-506.
22. Cohen SE, Todd PM. Stated and revealed preferences in companion animal choice. Behav Res Methods, 2019, 51(4): 1498-1509.
23. De Bekker-Grob E. Discrete choice experiments in health care: theory and applications. 2009.
24. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user's guide. Pharmacoeconomics, 2008, 26(8): 661-677.
25. Cascetta E. Transportation systems analysis: models and applications. Boston MA: Springer US, 2009.
26. King MT, Costa DS, Aaronson NK, et al. QLU-C10D: a health state classification system for a multi-attribute utility measure based on the EORTC QLQ-C30. Qual Life Res, 2016, 25(3): 625-636.
27. 谢诗桐. 基于方法对比的中国一般人群健康效用积分体系构建研究. 天津: 天津大学, 2020.
28. Bahrampour M, Byrnes J, Norman R, et al. Discrete choice experiments to generate utility values for multi-attribute utility instruments: a systematic review of methods. Eur J Health Econ, 2020, 21(7): 983-992.
29. Bhattacherjee A. Social science research: principles, methods and practices (revised edition). 2019.
30. Zhu W, Zhang M, Pan J, et al. Valuing Chinese medicine quality of life-11 dimensions (CQ-11D) health states using a discrete choice experiment with survival duration (DCETTO). Health Qual Life Outcomes, 2023, 21(1): 99.
31. Sawtooth Software. Sample size issues for conjoint analysis studies. 1998.
32. de Bekker-Grob EW, Donkers B, Jonker MF, et al. Sample size requirements for discrete-choice experiments in healthcare: a practical guide. Patient, 2015, 8(5): 373-384.
33. Demirkale F, Donovan D, Street DJ. Constructing D-optimal symmetric stated preference discrete choice experiments. J Stat Plan Infer, 2013, 143(8): 1380-1391.
34. Alamri AS, Georgiou S, Stylianou S. Discrete choice experiments: an overview on constructing D-optimal and near-optimal choice sets. Heliyon, 2023, 9(7): e18256.
35. 伍红艳, 孙利华, 任晓晓, 等. 疾病特异性生命质量量表效用值积分体系的构建方法及探讨. 中国卫生经济, 2012, 31(3): 7-10.
36. Robinson A, Spencer A, Moffatt P. A framework for estimating health state utility values within a discrete choice experiment: modeling risky choices. Med Decis Making, 2015, 35(3): 341-350.
37. Sutton AJ, Abrams KR. Bayesian methods in meta-analysis and evidence synthesis. Stat Methods Med Res, 2001, 10(4): 277-303.
38. Higgins JP, Green S. Cochrane handbook for systematic reviews of interventions. 2008.
39. Vass C, Rigby D, Campbell S, et al. Investigating the framing of risk attributes in a discrete choice experiment: an application of eye-tracking and think aloud. The 36th meeting of the Society for Medical Decision Making, 2014.
40. Krucien N, Ryan M, Hermens F. Using eye-tracking methods to inform decision making processes in discrete choice experiments, Health Economists’ Study Group (HESG). 2014.
41. 国家统计局. 中国统计年鉴2023年. 2024.

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