Optimizing the attribute selection process for stated preference study: a study based on best-worst scaling_Chinese Journal of Evidence-Based Medicine

Authors：

TAO Ying ^1,2 ,  LIU Shimeng ^1,2 , CHEN Yingyao ^1,2

1. School of Public Health, Fudan University, Shanghai 200032, P. R. China;
2. NHC Key Laboratory of Health Technology Assessment (Fudan University), Shanghai 200032, P. R. China;

Corresponding author：

LIU Shimeng, Email: smliu19@fudan.edu.cn

Keywords：

Stated preference; Discrete choice experiment; Best-worst scaling; Attribute selection; Type 2 diabetes mellitus; Second-line antihyperglycemic medications

DOI：

10.7507/1672-2531.202312099

Video：

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Objective To explore how to determine the attributes of stated preference research more scientifically and reasonably. Methods Based on the best-worst scaling object case (BWS-1) method, a BWS-1 questionnaire was generated using a balanced incomplete block design. Data collection was conducted among type 2 diabetes mellitus (T2DM) patients in Hainan and Jiangsu provinces. Data analysis was performed using counting analysis and conditional logit model to obtain the priority order of each attribute. Results The results of BWS-1 using the counting and modelling approach showed high consistency. Among the 11 attributes, the top three attributes influencing the preference for second-line antihyperglycemic medications selection in T2DM patients were blood glucose control effectiveness, cardiovascular protection capability, and risk of hypoglycemic events, while the last three factors were dosing frequency, mode of administration and bone fracture. Based on literature review, qualitative research, and BWS-1 results, the seven attributes of discrete choice experiment and best-worst scaling profile case (BWS-2) were determined as follows: treatment efficacy, weight change, hypoglycemic events, gastrointestinal side effects, cardiovascular health, mode of administration and out-of-pocket cost. Conclusion BWS-1 can serve as an effective tool for determining the attributes of stated preference research. However, it is not recommended to solely rely on the priority ranking of BWS-1 results to determine the scope of attributes for stated preference research. It is necessary to conduct a specific analysis in conjunction with the research's policy objectives and real-world circumstances.

Citation： TAO Ying, LIU Shimeng, CHEN Yingyao. Optimizing the attribute selection process for stated preference study: a study based on best-worst scaling. Chinese Journal of Evidence-Based Medicine, 2024, 24(9): 1079-1084. doi: 10.7507/1672-2531.202312099 Copy

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13.	Louviere JJ, Woodworth GG. Best-worst scaling: a model for largest difference judgments, working paper. University of Alberta, 1990.
14.	Liu S, Liu J, Yu Y, et al. What is valued most by patients with type 2 diabetes mellitus when selecting second-line antihyperglycemic medications in China. Front Pharmacol, 2021, 12: 802897.
15.	Pearmin D, Kroes J, Bradley M. Stated preference techniques: a guide to practice. Londra: Steer Davies Gleave and Hague Consulting Group, 1991.
16.	Flynn TN, Louviere JJ, Peters TJ, et al. Best-worst scaling: what it can do for health care research and how to do it. J Health Econ, 2007, 26(1): 171-189.
17.	Whitty JA, Oliveira Gonçalves AS. A systematic review comparing the acceptability, validity and concordance of discrete choice experiments and best-worst scaling for eliciting preferences in healthcare. Patient, 2018, 11(3): 301-317.
18.	Xu RH, Zhou LM, Wong EL, et al. Investigating medical student's preferences for internet-based healthcare services: a best-worst scaling survey. Front Public Health, 2021, 9: 757310.
19.	Yeh CH, Hartmann M, Langen N. The role of trust in explaining food choice: combining choice experiment and attribute best-worst scaling. Foods, 2020, 9(1): 45.
20.	Webb EJD, Meads D, Lynch Y, et al. Attribute selection for a discrete choice experiment incorporating a best-worst scaling survey. Value Health, 2021, 24(4): 575-584.
21.	Dave CV, Schneeweiss S, Kim D, et al. Sodium-glucose cotransporter-2 inhibitors and the risk for severe urinary tract infections: a population-based cohort study. Ann Intern Med, 2019, 171(4): 248-256.
22.	Zhang J, Johnson FR, Mohamed AF, et al. Too many attributes: a test of the validity of combining discrete-choice and best–worst scaling data. J Choice Model, 2015, 15: 1-13.
23.	Ali S, Ronaldson S. Ordinal preference elicitation methods in health economics and health services research: using discrete choice experiments and ranking methods. Br Med Bull, 2012, 103(1): 21-44.
24.	Clark MD, Determann D, Petrou S, et al. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics, 2014, 32(9): 883-902.
25.	Cheung KL, Wijnen BF, Hollin IL, et al. Using best-worst scaling to investigate preferences in health care. Pharmacoeconomics, 2016, 34(12): 1195-1209.
26.	Janssen EM, Benz HL, Tsai JH, et al. Identifying and prioritizing concerns associated with prosthetic devices for use in a benefit-risk assessment: a mixed-methods approach. Expert Rev Med Devices, 2018, 15(5): 385-398.
27.	Louviere J, Lings I, Islam T, et al. An introduction to the application of (case 1) best-worst scaling in marketing research. Int J Res Mark, 2013, 30(3): 292-303.

1. Soekhai V, Whichello C, Levitan B, et al. Methods for exploring and eliciting patient preferences in the medical product lifecycle: a literature review. Drug Discov Today, 2019, 24(7): 1324-1331.
2. 刘世蒙, 杨毅, 孙辉, 等. 患者偏好证据应用于卫生技术评估的价值、方法及挑战. 中国卫生政策研究, 2022, 15(5): 52-58.
3. Cowie L, Bouvy J. Measuring patient preferences: an exploratory study to determine how patient preferences data could be used in health technology assessment (HTA). Myeloma UK, 2019.
4. National Institute for Health and Care Excellence. Our research work. 2019.
5. Gonzalez JM. A guide to measuring and interpreting attribute importance. Patient, 2019, 12(3): 287-295.
6. Coast J, Al-Janabi H, Sutton EJ, et al. Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ, 2012, 21(6): 730-741.
7. Mangham LJ, Hanson K, McPake B. How to do (or not to do)..designing a discrete choice experiment for application in a low-income country. Health Policy Plan, 2009, 24(2): 151-158.
8. Abiiro GA, Leppert G, Mbera GB, et al. Developing attributes and attribute-levels for a discrete choice experiment on micro health insurance in rural Malawi. BMC Health Serv Res, 2014, 14: 235.
9. Mcfadden D. Conditional logit analysis of qualitative choice behavior. New York: Academic Press, 1974.
10. Thurstone LL. A law of comparative judgment. Psych Rev, 1927: 273-286.
11. Finn A, Louviere JJ. Determining the appropriate response to evidence of public concern: the case of food safety. J Pub Policy Market, 1992, 11(2): 12-25.
12. Szeinbach SL, Barnes JH, Mcghan WF, et al. Using conjoint analysis to evaluate health state preferences. Drug Info J, 1999, 33(3): 849-858.
13. Louviere JJ, Woodworth GG. Best-worst scaling: a model for largest difference judgments, working paper. University of Alberta, 1990.
14. Liu S, Liu J, Yu Y, et al. What is valued most by patients with type 2 diabetes mellitus when selecting second-line antihyperglycemic medications in China. Front Pharmacol, 2021, 12: 802897.
15. Pearmin D, Kroes J, Bradley M. Stated preference techniques: a guide to practice. Londra: Steer Davies Gleave and Hague Consulting Group, 1991.
16. Flynn TN, Louviere JJ, Peters TJ, et al. Best-worst scaling: what it can do for health care research and how to do it. J Health Econ, 2007, 26(1): 171-189.
17. Whitty JA, Oliveira Gonçalves AS. A systematic review comparing the acceptability, validity and concordance of discrete choice experiments and best-worst scaling for eliciting preferences in healthcare. Patient, 2018, 11(3): 301-317.
18. Xu RH, Zhou LM, Wong EL, et al. Investigating medical student's preferences for internet-based healthcare services: a best-worst scaling survey. Front Public Health, 2021, 9: 757310.
19. Yeh CH, Hartmann M, Langen N. The role of trust in explaining food choice: combining choice experiment and attribute best-worst scaling. Foods, 2020, 9(1): 45.
20. Webb EJD, Meads D, Lynch Y, et al. Attribute selection for a discrete choice experiment incorporating a best-worst scaling survey. Value Health, 2021, 24(4): 575-584.
21. Dave CV, Schneeweiss S, Kim D, et al. Sodium-glucose cotransporter-2 inhibitors and the risk for severe urinary tract infections: a population-based cohort study. Ann Intern Med, 2019, 171(4): 248-256.
22. Zhang J, Johnson FR, Mohamed AF, et al. Too many attributes: a test of the validity of combining discrete-choice and best–worst scaling data. J Choice Model, 2015, 15: 1-13.
23. Ali S, Ronaldson S. Ordinal preference elicitation methods in health economics and health services research: using discrete choice experiments and ranking methods. Br Med Bull, 2012, 103(1): 21-44.
24. Clark MD, Determann D, Petrou S, et al. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics, 2014, 32(9): 883-902.
25. Cheung KL, Wijnen BF, Hollin IL, et al. Using best-worst scaling to investigate preferences in health care. Pharmacoeconomics, 2016, 34(12): 1195-1209.
26. Janssen EM, Benz HL, Tsai JH, et al. Identifying and prioritizing concerns associated with prosthetic devices for use in a benefit-risk assessment: a mixed-methods approach. Expert Rev Med Devices, 2018, 15(5): 385-398.
27. Louviere J, Lings I, Islam T, et al. An introduction to the application of (case 1) best-worst scaling in marketing research. Int J Res Mark, 2013, 30(3): 292-303.

Chinese Journal of Evidence-Based Medicine

Optimizing the attribute selection process for stated preference study: a study based on best-worst scaling

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