Using Best–Worst Scaling to Investigate Preferences in Health Care

PharmacoEconomics

Volumn 34, Issue 12, 2016, Pages 1195-1209

  Cheung, Kei Long ^a   Wijnen, Ben F M ^a,b   Hollin, Ilene L ^c   Janssen, Ellen M ^c   Bridges, John F ^c   Evers, Silvia M A A ^a   Hiligsmann, Mickael ^a  

^a MAASTRICHT UNIVERSITY   (Netherlands)

^b EPILEPSY CENTER KEMPENHAEGHE   (Netherlands)

^c JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BEST WORST SCALING; CONSUMER; DECISION MAKING; HEALTH CARE; HEALTH CARE PERSONNEL; HUMAN; PATIENT PREFERENCE; PRIORITY JOURNAL; RATING SCALE; REVIEW; SYSTEMATIC REVIEW; HEALTH CARE DELIVERY; METHODOLOGY; PROCEDURES; SAMPLE SIZE;

CHOICE BEHAVIOR; DECISION MAKING; DELIVERY OF HEALTH CARE; HUMANS; PATIENT PREFERENCE; RESEARCH DESIGN; SAMPLE SIZE;

EID: 84978100628     PISSN: 11707690     EISSN: 11792027     Source Type: Journal    
DOI: 10.1007/s40273-016-0429-5     Document Type: Review

References (86)

1. Bridges JF, Jones C. Patient-based health technology assessment: a vision of the future. Int J Technol Assess Health Care. 2007;23(1):30–5.
2. Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Appl Health Econ Health Policy. 2003;2(1):55–64.
3. Bridges JF, Hauber AB, Marshall D, et al. Conjoint analysis applications in health—a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health. 2011;14(4):403–13.
4. Janz NK, Wren PA, Copeland LA, et al. Patient–physician concordance: preferences, perceptions, and factors influencing the breast cancer surgical decision. J Clin Oncol. 2004;22(15):3091–8.
5. Facey K, Boivin A, Gracia J, et al. Patients’ perspectives in health technology assessment: a route to robust evidence and fair deliberation. Int J Technol Assess Health Care. 2010;26(3):334–40.
6. Bridges JF, Hauber AB, Marshall D, et al. Conjoint analysis applications in health—a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health. 2011;14(4):403–13.
7. Green PE, Srinivasan V. Conjoint analysis in consumer research: issues and outlook. J Consum Res. 1978;5:103–23.
8. Bridges JF, Kinter ET, Kidane L, et al. Things are looking up since we started listening to patients. Patient. 2008;1(4):273–82.
9. Marley AA, Louviere JJ. Some probabilistic models of best, worst, and best–worst choices. J Math Psychol. 2005;49(6):464–80.
10. Finn A, Louviere JJ. Determining the appropriate response to evidence of public concern: the case of food safety. J Public Policy Mark. 1992;11(2):12–25.
11. Szeinbach SL, Barnes JH, McGhan WF, et al. Using conjoint analysis to evaluate health state preferences. Drug Inf J. 1999;33(3):849–58.
12. McIntosh E, Louviere JJ. Separating weight and scale value: an exploration of best-attribute scaling in health economics. Odense: Health Econ Study Group Meet; 2002.
13. 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):171–89.
14. 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.
15. de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21(2):145–72.
16. Flynn TN. Valuing citizen and patient preferences in health: recent developments in three types of best–worst scaling. Exp Rev Pharmacoecon Outcomes Res. 2010;10(3):259–67.
17. Mühlbacher AC, Kaczynski A, Zweifel P, et al. Experimental measurement of preferences in health and healthcare using best–worst scaling: an overview. Health Econ Rev. 2016;6(1):1–14.
18. Mühlbacher AC, Zweifel P, Kaczynski A, et al. Experimental measurement of preferences in health care using best–worst scaling (BWS): theoretical and statistical issues. Health Econ Rev. 2016;6(1):1–12.
19. Louviere JJ, Flynn TN, Marley A. Best–worst scaling: theory, methods and applications. Cambridge: Cambridge University Press; 2015.
20. de Bekker-Grob EW, Ryan M, Gerard K. Discrete choice experiments in health economics: a review of the literature. Health Econ. 2012;21(2):145–72.
21. Joy SM, Little E, Maruthur NM, et al. Patient preferences for the treatment of type 2 diabetes: a scoping review. Pharmacoeconomics. 2013;31(10):877–92.
22. Lancsar E, Louviere J, Donaldson C, et al. Best worst discrete choice experiments in health: methods and an application. Soc Sci Med. 2013;76(1):74–82.
23. Potoglou D, Burge P, Flynn T, et al. Best–worst scaling vs. discrete choice experiments: an empirical comparison using social care data. Soc Sci Med. 2011;72(10):1717–27.
24. Severin F, Schmidtke J, Mühlbacher A, et al. Eliciting preferences for priority setting in genetic testing: a pilot study comparing best–worst scaling and discrete-choice experiments. Eur J Hum Genet. 2013;21(11):1202–8.
25. Louviere JJ, Islam T. A comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best–worst scaling. J Bus Res. 2008;61(9):903–11.
26. Gallego G, Dew A, Lincoln M, et al. Should I stay or should I go? Exploring the job preferences of allied health professionals working with people with disability in rural Australia. Hum Resour Health. 2015;13(1):53.
27. Lancsar E, Louviere J, Donaldson C, et al. Best worst discrete choice experiments in health: methods and an application. Soc Sci Med. 2013;76:74–82.
28. 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):373–84.
29. Ghijben P, Lancsar E, Zavarsek S. Preferences for oral anticoagulants in atrial fibrillation: a best–best discrete choice experiment. Pharmacoeconomics. 2014;32(11):1115–27.
30. Meyfroidt S, Hulscher M, De Cock D, et al. A maximum difference scaling survey of barriers to intensive combination treatment strategies with glucocorticoids in early rheumatoid arthritis. Clin Rheumatol. 2015;34(5):861–9.
31. Van Hulst L, Kievit W, Van Bommel R, et al. Rheumatoid arthritis patients and rheumatologists approach the decision to escalate care differently: results of a maximum difference scaling experiment. Arthritis Care Res. 2011;63(10):1407–14.
32. Louviere JJ, Flynn TN. Using best–worst scaling choice experiments to measure public perceptions and preferences for healthcare reform in Australia. Patient. 2010;3(4):275–83.
33. Kurkjian TJ, Kenkel JM, Sykes JM, et al. Impact of the current economy on facial aesthetic surgery. Aesthet Surg J. 2011;31(7):770–4.
34. Gallego G, Bridges JFP, Flynn T, et al. Using best–worst scaling in horizon scanning for hepatocellular carcinoma technologies. Int J Technol Assess Health Care. 2012;28(3):339–46.
35. Marti J. A best–worst scaling survey of adolescents’ level of concern for health and non-health consequences of smoking. Soc Sci Med. 2012;75(1):87–97.
36. Mazanov J, Huybers T, Connor J. Prioritising health in anti-doping: what Australians think. J Sci Med Sport. 2012;15(5):381–5.
37. Silverman S, Calderon A, Kaw K, et al. Patient weighting of osteoporosis medication attributes across racial and ethnic groups: a study of osteoporosis medication preferences using conjoint analysis. Osteoporos Int. 2013;24(7):2067–77.
38. Cozmuta R, Merkel PA, Wahl E, et al. Variability of the impact of adverse events on physicians’ decision making. BMC Med Inform Decis Mak. 2014;14:86.
39. Ejaz A, Spolverato G, Bridges JF, et al. Choosing a cancer surgeon: analyzing factors in patient decision making using a best–worst scaling methodology. Ann Surg Oncol. 2014;21(12):3732–8.
40. Hauber AB, Mohamed AF, Johnson FR, et al. Understanding the relative importance of preserving functional abilities in Alzheimer’s disease in the United States and Germany. Qual Life Res. 2014;23(6):1813–21.
41. Hofstede SN, van Bodegom-Vos L, Wentink MM, et al. Most important factors for the implementation of shared decision making in sciatica care: ranking among professionals and patients. PLoS ONE. 2014;9(4):e94176.
42. Torbica A, De Allegri M, Belemsaga D, et al. What criteria guide national entrepreneurs’ policy decisions on user fee removal for maternal health care services? Use of a best–worst scaling choice experiment in West Africa. J Health Serv Res Policy. 2014;19(4):208–15.
43. van Til J, Groothuis-Oudshoorn C, Lieferink M, et al. Does technique matter; a pilot study exploring weighting techniques for a multi-criteria decision support framework. Cost Eff Resour Alloc. 2014;12(1):22.
44. Yuan Z, Levitan B, Burton P, et al. Relative importance of benefits and risks associated with antithrombotic therapies for acute coronary syndrome: patient and physician perspectives. Curr Med Res Opin. 2014;30(9):1733–41.
45. Beusterien K, Kennelly MJ, Bridges JF, et al. Use of best-worst scaling to assess patient perceptions of treatments for refractory overactive bladder. Neurourol Urodyn. 2015. doi:10.1002/nau.22876 (Epub 2015 Sep 14).
46. Fraenkel L, Lim J, Garcia-Tsao G, et al. Variation in treatment priorities for chronic hepatitis C: a latent class analysis. Patient. 2016;9(3):241–9.
47. Hashim H, Beusterien K, Bridges JF, et al. Patient preferences for treating refractory overactive bladder in the UK. Int Urol Nephrol. 2015;47(10):1619–27.
48. Malhotra C, Chan N, Zhou J, et al. Variation in physician recommendations, knowledge and perceived roles regarding provision of end-of-life care. BMC Palliat Care. 2015;14(1):52.
49. Narurkar V, Shamban A, Sissins P, et al. Facial treatment preferences in aesthetically aware women. Dermatol Surg. 2015;41(Suppl 1):S153–60.
50. Peay HL, Hollin I, Bridges J. Prioritizing parental worry associated with Duchenne muscular dystrophy using best–worst scaling. J Genet Couns. 2016;25(2):305–13.
51. Ross M, Bridges JFP, Ng X, et al. A best–worst scaling experiment to prioritize caregiver concerns about ADHD medication for children. Psychiatr Serv. 2015;66(2):208–11.
52. Wittenberg E, Bharel M, Saada A, et al. Measuring the preferences of homeless women for cervical cancer screening interventions: development of a best–worst scaling survey. Patient. 2015;8(5):455–67. doi:10.1007/s40271-014-0110-z (Epub 2015).
53. Yan K, Bridges JF, Augustin S, et al. Factors impacting physicians’ decisions to prevent variceal hemorrhage. BMC Gastroenterol. 2015;15:55.
54. Yu T, Holbrook JT, Thorne JE, et al. Outcome preferences in patients with noninfectious uveitis: results of a best–worst scaling study. Invest Ophthalmol Vis Sci. 2015;56(11):6864–72.
55. Coast J, Flynn TN, Salisbury C, et al. Maximising responses to discrete choice experiments: a randomised trial. Appl Health Econ Health Policy. 2006;5(4):249–60.
56. Coast J, Flynn TN, Natarajan L, et al. Valuing the ICECAP Capability Index for older people. Soc Sci Med. 2008;67(5):874–82.
57. Flynn TN, Louviere JJ, Peters TJ, et al. Estimating preferences for a dermatology consultation using best–worst scaling: comparison of various methods of analysis. BMC Med Res Methodol. 2008;8:76.
58. Al-Janabi H, Flynn TN, Coast J. Estimation of a preference-based carer experience scale. Med Decis Making. 2011;31(3):458–68.
59. Potoglou D, Burge P, Flynn T, et al. Best–worst scaling vs. discrete choice experiments: an empirical comparison using social care data. Soc Sci Med. 2011;72(10):1717–27.
60. Ratcliffe J, Couzner L, Flynn T, et al. Valuing child health utility 9D health states with a young adolescent sample: a feasibility study to compare best–worst scaling discrete-choice experiment, standard gamble and time trade-off methods. Appl Health Econ Health Policy. 2011;9(1):15–27.
61. Knox SA, Viney RC, Street DJ, et al. What’s good and bad about contraceptive products? A best–worst attribute experiment comparing the values of women consumers and GPs. Pharmacoeconomics. 2012;30(12):1187–202.
62. Molassiotis A, Emsley R, Ashcroft D, et al. Applying best–worst scaling methodology to establish delivery preferences of a symptom supportive care intervention in patients with lung cancer. Lung cancer (Amsterdam, Netherlands). 2012;77(1):199–204.
63. Najafzadeh M, Lynd LD, Davis JC, et al. Barriers to integrating personalized medicine into clinical practice: a best–worst scaling choice experiment. Genet Med. 2012;14(5):520–6.
64. Netten A, Burge P, Malley J, et al. Outcomes of social care for adults: developing a preference-weighted measure. Health Technol Assess. 2012;16(16):1–165.
65. Ratcliffe J, Flynn T, Terlich F, et al. Developing adolescent-specific health state values for economic evaluation: an application of profile case best–worst scaling to the child health utility 9D. PharmacoEconomics. 2012;30(8):713–27.
66. Severin F, Schmidtke J, Mühlbacher A, et al. Eliciting preferences for priority setting in genetic testing: a pilot study comparing best–worst scaling and discrete-choice experiments. Eur J Hum Genet. 2013;21(11):1202–8.
67. Yoo HI, Doiron D. The use of alternative preference elicitation methods in complex discrete choice experiments. J Health Econ. 2013;32(6):1166–79.
68. Damery S, Biswas M, Billingham L, et al. Patient preferences for clinical follow-up after primary treatment for soft tissue sarcoma: a cross-sectional survey and discrete choice experiment. Eur J Surg Oncol. 2014;40(12):1655–61.
69. Hollin IL, Peay HL, Bridges JF. Caregiver preferences for emerging Duchenne muscular dystrophy treatments: a comparison of best–worst scaling and conjoint analysis. Patient. 2015;8(1):19–27.
70. Peay HL, Hollin I, Fischer R, et al. A community-engaged approach to quantifying caregiver preferences for the benefits and risks of emerging therapies for Duchenne muscular dystrophy. Clin therapeutics. 2014;36(5):624–37.
71. Ratcliffe J. A think aloud study comparing the validity and acceptability of discrete choice and best worst scaling methods. PLoS ONE. 2014;9(4):e90635.
72. Ungar WJ, Hadioonzadeh A, Najafzadeh M, et al. Quantifying preferences for asthma control in parents and adolescents using best–worst scaling. Respir Med. 2014;108(6):842–51.
73. Whitty JA, Ratcliffe J, Chen G, et al. Australian public preferences for the funding of new health technologies: a comparison of discrete choice and profile case best–worst scaling methods. Med Decis Making. 2014;34(5):638–54.
74. dosReis S, Ng X, Frosch E, et al. Using best–worst scaling to measure caregiver preferences for managing their child’s ADHD: a pilot study. Patient. 2015;8(5):423–31.
75. Flynn TN, Huynh E, Peters TJ, et al. Scoring the ICECAP—a capability instrument. Estimation of a UK general population tariff. Health Econ. 2015;24(3):258–69.
76. Franco MR, Howard K, Sherrington C, et al. Eliciting older people’s preferences for exercise programs: a best–worst scaling choice experiment. J Physiother. 2015;61(1):34–41.
77. Gendall P, Hoek J, Taylor R, et al. Should support for obesity interventions or perceptions of their perceived effectiveness shape policy? Aust N Z J Public Health. 2015;39(2):172–6.
78. Jones LG, Hawkins GE, Brown SD. Using best–worst scaling to improve psychological service delivery: an innovative tool for psychologists in organized care settings. Psychol Serv. 2015;12(1):20–7.
79. O’Hara NN, Roy L, O’Hara LM, et al. Healthcare worker preferences for active tuberculosis case finding programs in South Africa: a best–worst scaling choice experiment. PLoS ONE. 2015;10(7):e0133304.
80. Ratcliffe J, Huynh E, Stevens K, Brazier J, Sawyer M, Flynn T. Nothing about us without us? A comparison of adolescent and adult health-state values for the Child Health Utility-9D using profile case best–worst scaling. Health Econ. 2016;25(4):486–96. doi:10.1002/hec.3165.
81. Tsao NW, Khakban A, Gastonguay L, et al. Perceptions of British Columbia residents and their willingness to pay for medication management services provided by pharmacists. Can Pharm J. 2015;148(5):263–73.
82. Brown TM, Pashos CL, Joshi AV, et al. The perspective of patients with haemophilia with inhibitors and their care givers: preferences for treatment characteristics. Haemophilia. 2011;17(3):476–82.
83. Hoek J, Wong C, Gendall P, et al. Effects of dissuasive packaging on young adult smokers. Tob Control. 2011;20(3):183–8.
84. Cameron MP, Newman PA, Roungprakhon S, et al. The marginal willingness-to-pay for attributes of a hypothetical HIV vaccine. Vaccine. 2013;31(36):3712–7.
85. Maubach N, Hoek J, Mather D. Interpretive front-of-pack nutrition labels: comparing competing recommendations. Appetite. 2014;82:67–77.
86. Xie F, Pullenayegum E, Gaebel K, et al. Eliciting preferences to the EQ-5D-5L health states: discrete choice experiment or multiprofile case of best–worst scaling? Eur J Health Econ. 2014;15(3):281–8.