Soekhai V, de Bekker-Grob E, Ellis A, Vass C. Discrete choice experiments in health economics: past, present and future. Pharmacoeconomics. 2019;37(2):201–26.
Article
Google Scholar
Brazier J, Ratcliffe J, Salomon J, Tsuchiya A. Measuring and valuing health benefits for economic evaluation. 2nd ed. Oxford: Oxford University Press; 2017.
Google Scholar
Whitty J, Lancsar E, Rixon K, Golenko X, Ratcliffe J. A systematic review of stated preference studies reporting public preferences for healthcare priority setting. Patient. 2014;7(4):365–86.
Article
Google Scholar
Gu Y, Lancsar E, Ghijben P, Butler J, Donaldson C. Attributes and weights in health care priority setting: a systematic review of what counts and to what extent. Soc Sci Med. 2015;146:41–52.
Article
Google Scholar
Fryback D, Dasbach E, Klein R, et al. The Beaver Dam Health Outcomes Study: initial catalog of health state quality factors. Med Decis Making. 1993;13(2):89–102.
Article
CAS
Google Scholar
Brazier J, Roberts J, Deverill M. The estimation of a preference-based measure of health from the SF-36. J Health Econ. 2002;21(2):271–92.
Article
Google Scholar
Craig B, Busschbach J, Salomon J. Keep it simple: ranking health states yields values similar to cardinal measurement approaches. J Clin Epidemiol. 2009;62(3):296–305.
Article
Google Scholar
Farmakas A, Theodorou M, Galanis P, et al. Public engagement in setting healthcare priorities: a ranking exercise in Cyprus. Cost Eff Resour Alloc. 2017;9(15):16.
Article
Google Scholar
Wiseman V, Mooney G, Berry G, Tang KC. Involving the general public in priority setting: experiences from Australia. Soc Sci Med. 2003;56(5):1001–12.
Article
CAS
Google Scholar
Kaplan G, Baron-Epel O. The public’s priorities in health services. Health Expect. 2015;18(5):904–17.
Article
Google Scholar
Finn A, Louviere J. Determining the appropriate response to evidence of public concern: the case of food safety. J Public Policy Marketing. 1992;11(2):12–25.
Article
Google Scholar
Lancsar E, Louviere J, Donaldson C, Currie G, Burgess L. Best worst discrete choice experiments in health: methods and an application. Soc Sci Med. 2013;76(1):74–82.
Article
Google Scholar
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.
Article
Google Scholar
Uy E, Bautista D, Xin X, et al. Using best-worst scaling choice experiments to elicit the most important domains of health for health-related quality of life in Singapore. PLoS One. 2018;13(2):e0189687.
Article
CAS
Google Scholar
Hauber A, Mohamed A, Johnson R, 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.
Article
Google Scholar
Louviere J, Street D, Burgess L, et al. Modelling the choices of individual decision makers be combining efficient choice experiment designs with extra preference information. J Choice Modelling. 2008;1:126–63.
Article
Google Scholar
Louviere J, Flynn T, Marley A. Best-worst scaling: theory, methods and applications. Cambridge: Cambridge University Press; 2015.
Book
Google Scholar
Wright S, Vass C, Sim G, et al. Accounting for scale heterogeneity in health care related discrete choice experiments when comparing stated preferences: a systematic review. Patient. 2018;11:475–88.
Article
Google Scholar
Vass C, Wright S, Burton M, Payne K. Scale heterogeneity in healthcare discrete choice experiments: a primer. Patient. 2018;11:167–73.
Article
Google Scholar
Ratcliffe J, Lancsar E, Flint T, et al. Does one size fit all? Assessing the preferences of older and younger people for attributes of quality of life. Qual Life Res. 2017;26(2):299–309.
Article
Google Scholar
Hawthorne G, Richardson J, Osborne R. The Assessment of Quality of Life (AQoL) instrument: a psychometric measure of health-related quality of life. Qual Life Res. 1999;8(3):209–24.
Article
CAS
Google Scholar
Netten A, Burge P, Malley J, et al. Outcomes of social care for adults: developing a preference-weighted measure. Health Technol Assess. 2001;16:1–165.
Google Scholar
Pink B. Information paper: an introduction to Socio-Economic Indexes for Areas (SEIFA). Cat no. 2039.0. Canberra (ACT): Australian Bureau of Statistics; 2006.
Pink B. Socio-Economic Indexes for Areas (SEIFA)-Technical Paper. Cat no. 2039.0.55.001. Canberra (ACT: Australian Bureau of Statistics; 2006A.
Greene W, Hensher D. Does scale heterogeneity across individuals’ matter? An empirical assessment of alternative logit models. Transportation. 2010;37(3):413–28.
Article
Google Scholar
Lancsar E, Fiebig D, Hole A. Discrete choice experiments: a guide to model specification, estimation and software. Pharmacoeconomics. 2017;35(7):697–716.
Article
Google Scholar
Harada C, Natelson Love M, Triebel K. Normal cognitive aging. Clin Geriatr Med. 2013;29(4):737–52.
Article
Google Scholar
Kumar S, Kant S. Exploded logit modelling of stakeholders’ preferences for multiple forest values. For Policy Econ. 2007;9(5):515–26.
Article
Google Scholar
Swait J, Louviere J. The role of the scale parameter in the estimation and comparison of multinomial logit models. J Mark Res. 1993;30(3):305–14.
Article
Google Scholar
Greene W. Econometric analysis. 7th ed. Upper Saddle River: Prentice Hall; 2007.
Google Scholar
Fiebig D, Keane M, Louviere J, et al. The generalized multinomial logit: accounting for scale and coefficient heterogeneity. Mark Sci. 2010;29(3):393–421.
Article
Google Scholar
Akaike H. A new look at the statistical model identification. IEEE Transact Autom Control. 1974;19:716–23.
Article
Google Scholar
Australian Bureau of Statistics. Household use of information technology, Australia, 2016–17. Canberra (ACT): Australian Bureau of Statistics; 2018.
Google Scholar
Milte R, Ratcliffe J, Chen G, et al. Cognitive overload? An exploration of the potential impact of cognitive functioning in discrete choice experiments with older people in health care. Value Health. 2014;17(5):655–9.
Article
Google Scholar