Hostname: page-component-586b7cd67f-r5fsc Total loading time: 0 Render date: 2024-11-24T19:14:37.114Z Has data issue: false hasContentIssue false
  • English
  • Français

Choice experiments in health: the good, the bad, the ugly and toward a brighter future

Published online by Cambridge University Press:  01 October 2009

JORDAN J. LOUVIERE  and
EMILY LANCSAR
JORDAN J. LOUVIERE*
Affiliation:
Professor of Marketing, Executive Director, Centre for the Study of Choice, University of Technology, Sydney, Australia
EMILY LANCSAR
Affiliation:
Lecturer in Economics, Business School and Institute of Health and Society, Newcastle University, UK
*
*Corresponding author: Jordan J. Louviere, Professor of Marketing, Executive Director, Centre for the Study of Choice, University of Technology, Sydney, PO BOX 123, Broadway, NSW 2007, Australia. Email: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract:

Compared to many applied areas of economics, health economics has a strong tradition in eliciting and using stated preferences (SP) in policy analysis. Discrete choice experiments (DCEs) are one SP method increasingly used in this area. Literature on DCEs in health and more generally has grown rapidly since the mid-1990s. Applications of DCEs in health have come a long way, but to date few have been ‘best practice’, in part because ‘best practice’ has been somewhat of a moving target. The purpose of this paper is to briefly survey the history of DCEs and the state of current knowledge, identify and discuss knowledge gaps, and suggest potentially fruitful areas for future research to fill such gaps with the aim of moving the application of DCEs in health economics closer to best practice.

Type
Article
Information
Health Economics, Policy and Law , Volume 4 , Issue 4 , October 2009 , pp. 527 - 546
Copyright
Copyright © Cambridge University Press 2009

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adamowicz, W., Bunch, D., Cameron, T., Dellaert, B., Hanneman, M., Keane, M., Louviere, J., Meyer, R., Steenburgh, T. and Swait, J. (2008), ‘Behavioural frontiers in choice modelling’, Marketing Letters, 19: 215–228.CrossRefGoogle Scholar
Allenby, G. M. and Rossi, P. E. (1998), ‘Marketing models of consumer heterogeneity’, Journal of Econometrics, 89(1–2): 57–78.CrossRefGoogle Scholar
Anderson, D. A. and Wiley, J. (1992), ‘Efficient choice set designs for estimating cross effects models’, Marketing Letters, 3(4): 357–370.CrossRefGoogle Scholar
Anderson, N. H. (1981), Foundations of Information Integration Theory, New York: Academic Press.Google Scholar
Bech, M. (2003), ‘Politicians’ and hospital managers’ trade-offs in the choice of reimbursement scheme: a discrete choice experiment’, Health Policy, 66(3): 261–275.CrossRefGoogle ScholarPubMed
Bliemer, M., Rose, J. and Hess, S. (2008), ‘Approximation of Bayesian efficiency in experimental choice designs’, Journal of Choice Modelling, 1(1): 98–127.CrossRefGoogle Scholar
Bradley, R. A. and Terry, M. E. (1952), ‘Rank analysis of incomplete block designs: I. The method of paired comparisons’, Biometrika, 39: 324–345.Google Scholar
Brunswick, E. (1955), ‘Representative design and probabilistic theory in a functional psychology’, Psychological Review, 62: 193–217.CrossRefGoogle Scholar
Bunch, D., Louviere, J. and Anderson, D. (1996), ‘A comparison of experimental design strategies for multinomial logit models: The case of generica attributes’, University of California, Davis, CA.Google Scholar
Burgess, L. and Street, A. (2005), ‘Optimal designs for choice experiments with asymmetric attributes’, Journal of Statistical Planning and Inference, 134(1): 288–301.CrossRefGoogle Scholar
Burgess, L., Street, D., Viney, R. and Louviere, J. J. (2006), ‘Design of Choice Experiments in Health Economics’, in Jones, A. M. (ed.), The Elgar Companion to Health Economics, Cheltenham, UK: Edward Elgar: 415–426.Google Scholar
Cairns, J. and van der Pol, M. (2004), ‘Repeated follow-up as a method for reducing non-trading behaviour in discrete choice experiments’, Social Science and Medicine, 58(11): 2211–2218.CrossRefGoogle ScholarPubMed
Carlsson, F. and Martinsson, P. (2003), ‘Design techniques for stated preference methods in health economics’, Health Economics, 12: 281–294.CrossRefGoogle ScholarPubMed
Chakroborty, G., Ettensen, R.and Gaeth, G. (1994), ‘How consumers choose health insurance’, Journal of Helath Care Marketing, 14(1): 21–33.Google Scholar
Coast, J. and Horrocks, S. (2007), ‘Developing attributes and levels for discrete choice experiments using qualitative methods’, Journal of Health Services and Research Policy, 12(1): 25–30.CrossRefGoogle ScholarPubMed
David, H. A. (1988), The Method of Paired Comparisons, Lubrecht and Cramer, Ltd.Google Scholar
DeShazo, J. R. and Fermo, G. (2002), ‘Designing choice sets for stated preference methods: the effects of complexity on choice consistency’, Journal of Environmental Economics and Management, 44: 123–143.CrossRefGoogle Scholar
Drummond, M. F., Sculpher, M. J., Torrance, G. W., O'Brien, B. J. and Stoddart, G. L. (2005), Methods for the Economic Evaluation of Health Care Programmes, Oxford: Oxford University Press.CrossRefGoogle Scholar
Erdem, T. and Keane, M. (1996), ‘Decision-making under uncertainty: capturing dynamic brand choice processes in turbulent consumer goods markets’, Marking Science, 15(1): 1–20.CrossRefGoogle Scholar
Fiebig, D. G., Keane, M., Louviere, J. and Wasi, N. (conditionally accepted), ‘The generalised multinomial logit model’, Marketing Science.Google Scholar
Finn, A. and Louviere, J. (1992), ‘Determining the appropriate response to evidence of public concern: the case of food safety’, Journal of Public Policy and Marketing, 11(1): 12–25.CrossRefGoogle Scholar
Flynn, T., Louviere, J., Peters, T. and Coast, J. (2007), ‘Best–worst scaling: what it can do for health care and how to do it’, Journal of Health Economics, 26: 171–189.CrossRefGoogle Scholar
Green, P. and Rao, V. (1971), ‘Conjoint measurement for quantifying judgement data’, Journal of Marketing Research, 8: 355–363.Google Scholar
Greene, W. H., Hensher, D. A. and Rose, J. (2006), ‘Accounting for heterogeneity in the variance of unobserved effects in mixed logit models’, Transportation Research Part B: Methodological, 40(1): 75–92.CrossRefGoogle Scholar
Gyrd-Hansen, D. (2004), ‘Investigating the social value of health changes’, Journal of Health Economics, 23(6): 1101–1116.CrossRefGoogle ScholarPubMed
Hakim, Z. and Pathak, D. S. (1999), ‘Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling', Health Economics, 8(2): 103–116.3.0.CO;2-E>CrossRefGoogle Scholar
Hall, J., Fiebig, D. G., King, M. T., Hossain, I. and Louviere, J. J. (2006), ‘What influences participation in genetic carrier testing? Results from a discrete choice experiment’, Journal of Health Economics, 25(3): 520–537.CrossRefGoogle ScholarPubMed
Hall, J., Kenny, P., King, M., Louviere, J., Viney, R. and Yeoh, A. (2002), ‘Using stated preference discrete choice modelling to evaluate the introduction of Varicella vaccination’, Health Economics, 11: 457–465.CrossRefGoogle ScholarPubMed
Hole, A. R. (2008), ‘Modelling heterogeneity in patients’ preferences for the attributes of a general practitioner appointment’, Health Economics, 27: 1078–1094.CrossRefGoogle ScholarPubMed
Huber, J. and Zwerina, K. (1996), ‘The importance of utility balance in efficient choice designs’, Journal of Marketing Research, 33: 307–317.CrossRefGoogle Scholar
Islam, T., Louviere, J. J. and Burke, P. F. (2007), ‘Modeling the effects of including/excluding attributes in choice experiment on systematic and random components’, International Journal of Research in Marketing, 24: 289–300.CrossRefGoogle Scholar
Jan, S., Mooney, G., Ryan, M., Bruggemann, K. and Alexander, K. (2000), ‘The use of conjoint analysis to elicit community preferences in public health research: a case study of hospital services in South Australia’, Australian and New Zealand Journal of Public Health, 24: 64–70.CrossRefGoogle Scholar
Johnson, F. R., Banzhaf, M. R. and Desvousges, W. H. (2000), ‘Willingness to pay for improved respiratory and cardiovascular health: a multiple-format stated preference approach’, Health Economics, 9(4): 295–317.3.0.CO;2-D>CrossRefGoogle Scholar
Kamakura, W. A. and Russell, G. R. (1989), ‘A probabilistic choice model for market segmentation and elasticity structure’, Journal of Marketing Research, 26(4): 379–390.CrossRefGoogle Scholar
Kanninen, B. (2002), ‘Optimal designs for multinomial choice experiments’, Journal of Marketing Research, 39: 214–227.CrossRefGoogle Scholar
Keane, M. P. and Wolpin, K. I. (1994), ‘The solution and estimation of discrete choice dynamic programming models by simulation and interpolation: Monte Carlo evidence’, The Review of Economics Statistics, 76(4): 648–672.CrossRefGoogle Scholar
Kendall, M. G. and Smith, B. B. (1940), ‘On the method of paired comparisons’, Biometrika, 31: 324–345.CrossRefGoogle Scholar
King, M. T., Hall, J., Lancsar, E., Fiebig, D., Hossain, I., Louviere, J., Reddel, H. K. and Jenkins, C. R. (2007), ‘Patient preferences for managing asthma: results from a discrete choice experiment’, Health Economics, 16(7): 703–717.CrossRefGoogle ScholarPubMed
Kjaer, T. and Gyrd-Hansen, D. (2008), ‘Preference heterogeneity and choice of cardiac rehabilitation program: results from a discrete choice experiment’, Health Policy, 85: 124–132.CrossRefGoogle ScholarPubMed
Lancsar, E. and Louviere, J. (2005) ‘Several methods for dealing with scale confound and efficiency in stated preference data with an empirical illustration’, Health Economics Study Group, Newcastle upon Tyne, UK, 29 June–1 July.Google Scholar
Lancsar, E. and Louviere, J. (2006), ‘Deleting “irrational” responses from discrete choice experiments: a case of investigating or imposing preferences?’, Health Economics, 15(8): 797–811.CrossRefGoogle ScholarPubMed
Lancsar, E. and Louviere, J. (2008a), ‘Conducting discrete choice experiments to inform healthcare decision making: a user’s guide’, Pharmacoeconomics, 26(8): 661–677.CrossRefGoogle ScholarPubMed
Lancsar, E. and Louviere, J. (2008b), Distinguishing between Conjoint Analysis and Discrete Choice Experiments with Implications for Stated Preference and Welfare Elicitation, Sydney: University of Technology, Sydney.Google Scholar
Lancsar, E., Louviere, J. and Flynn, T. (2007a), ‘Several methods to investigate relative attribute impact in stated preference experiments’, Social Science and Medicine, 64(8): 1738–1753.CrossRefGoogle ScholarPubMed
Lancsar, E. and Savage, E. (2004a), ‘Deriving welfare measures from discrete choice experiments: inconsistency between current methods and random utility and welfare theory, Health Economics, 13(9): 901–907.CrossRefGoogle ScholarPubMed
Lancsar, E. and Savage, E. (2004b), ‘Deriving welfare measures from discrete choice experiments: a response to Ryan and Santos Silva’, Health Economics Letters, 13(9): 919–924.CrossRefGoogle Scholar
Lancsar, E. J., Hall, J. P., King, M., Kenny, P., Louviere, J. J., Fiebig, D. G., Hossain, I., Thien, F. C., Reddel, H. K. and Jenkins, C. R. (2007b), ‘Using discrete choice experiments to investigate subject preferences for preventive asthma medication’, Respirology, 12(1): 127–136.CrossRefGoogle ScholarPubMed
Lancsar, E. and Louviere, J. (2009), ‘Estimating individual level discrete choice models and welfare measures using best worst choice experiments’, Health Economics Study Group, Manchester, UK, 7–9 January.Google Scholar
Lloyd, A. J. (2003), ‘Threats to the estimation of benefit: are preference elicitation methods accurate?’, Health Economics, 12: 393–402.CrossRefGoogle Scholar
Longo, M. F., Cohen, D. R., Hood, K., Edwards, A., Robling, G. E. and Russell, I. T. (2006), ‘Involving patients in primary care consultations: assessing preferences using discrete choice experiments’, British Journal of General Practice, 56: 35–45.Google ScholarPubMed
Louviere, J. (2004), Complex Statistical Choice Models: Are the Assumptions True and If Not, What Are The Consequences?, Sydney: University of Technology, Sydney.Google Scholar
Louviere, J. and Engle, T. (2006), ‘Confound it! That pesky little scale constant messes up our convenient assumptions!’, Sawtooth Software Conference, Sequim, USA.Google Scholar
Louviere, J., Islam, T., Wasi, N., Street, D. and Burgess, L. (2008), ‘Designing discrete choice experiments: do optimal designs come at a price?’ Journal of Consumer Research, 35: 360–376.CrossRefGoogle Scholar
Louviere, J., Pihlens, D., Lancsar, E. and Carson, R. (2008a), Examples of Design Practices: A Work in Progress, Sydney: University of Technology, Sydney.Google Scholar
Louviere, J., Street, A., Burgess, L., Wasi, N., Islam, T.and Marley, A. A. J. (2008b), ‘Modeling the choices of individual decision-makers by combining efficient choice experiment designs with extra preference information’, The Journal of Choice Modelling, 1(1): 128–163.CrossRefGoogle Scholar
Louviere, J. and Woodworth, G. (1983), ‘Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregated data’, Journal of Marketing Research, 20: 350–367.CrossRefGoogle Scholar
Louviere, J. J., Burgess, L., Street, A. and Marley, A. A. J. (2004), Modeling the Choice of Single Individuals By Combining Efficient Choice Experiment Designs with Extra Preference Information, Sydney: University of Technology, Sydney.Google Scholar
Louviere, J. J., Hensher, D. A. and Swait, J. D. (2000), Stated Choice Methods Analysis and Application, Cambridge: Cambridge University Press.CrossRefGoogle Scholar
Louviere, J. J. and Meyer, R. J. (2007), ‘Formal choice models of informal choices: what choice modeling research can (and can’t) learn from behavioural theory’, Review of Marketing Research, 4: 3–32.CrossRefGoogle Scholar
Luce, R. D. (1959), Individual Choice Behaviour, New York: Wiley.Google Scholar
Luce, R. D. and Suppes, P. (1965), ‘Preference, Utility and Subjective Probability’, in Luce, R. D., Bush, R. R. and Galanter, E. (eds), Handbook of Mathematical Psychology, volume III, New York: Wiley: 235–406.Google Scholar
Maddala, T., Phillips, K. and Johnson, F. R. (2003), ‘An experiment on simplifying conjoint analysis designs for measuring preferences’, Health Economics, 12: 1035–1047.CrossRefGoogle Scholar
Mark, T. and Swait, J. (2004), ‘Using stated preference and revealed preference modelling to evaluate prescribing decisions’, Health Economics, 13(6): 563–573.CrossRefGoogle ScholarPubMed
Marley, A. and Louviere, J. (2005), ‘Some probabilistic models of best, worst, and best–worst choices’, Journal of Mathematical Psychology, 49(6): 464–480.CrossRefGoogle Scholar
Marley, A. A. J., Flynn, T.and Louviere, J. J. (2008), ‘Probabilistic models of set-dependent and attribute-level best–worst choice’, Journal of Mathematical Psychology, 52: 281–296.CrossRefGoogle Scholar
McFadden, D. (1973), ‘Conditional logit analysis of qualitative choice behavior’, in Zarembka, P. (ed.), Frontiers of Econometrics, New York: Academic Press: 105–142.Google Scholar
McIntosh, E. (2006), ‘Using discrete choice experiments within a cost–benefit analysis framework: some considerations’, Pharmacoeconomics, 24(9): 855–868.CrossRefGoogle ScholarPubMed
McIntosh, E. and Louviere, J. (2002), Health Economics Study Group Meeting, Brunel University, January.Google Scholar
Morgan, A., Shackley, P., Pickin, M. and Brazier, J. (2000), ‘Quantifying patient preferences for out-of-hours care’, Journal of Health Services Research and Policy, 5(4): 214–218.CrossRefGoogle Scholar
Propper, C. (1990), ‘Contingent valuation of time spent on NHS waiting lists’, Economic Journal, 100: 193–199.CrossRefGoogle Scholar
Restle, F. (1961), Psychology of Judgement and Choice: A Theoretical Essay, New York: Wiley.Google Scholar
Revelt, D. and Train, K. (1998), ‘Mixed logit with repeated choices of appliance efficiency levels’, Review of Economic Statistics, 80(4): 647–657.CrossRefGoogle Scholar
Ruta, D., Mitton, C., Bate, A. and Donaldson, C. (2005), ‘Programme budgetting and marginal analysis (PBMA): bridging the divide between doctors and managers’, British Medical Journal, 330: 1501–1503.CrossRefGoogle Scholar
Ryan, M. (1999), ‘Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation’, Social Science and Medicine, 48(4): 535–546.CrossRefGoogle Scholar
Ryan, M. and Gerard, K. (2003), ‘Using discrete choice experiments to value health care programmes: current practice and future research reflections’, Applied Health Economics and Health Policy, 2(1): 55–64.Google ScholarPubMed
Ryan, M., Gerard, K. and Amaya-Amaya, M. (2008), Using Discrete Choice Experiments to Value Helath and Health Care, Dordrecht: Springer.CrossRefGoogle Scholar
Ryan, M. and Hughes, J. (1997), ‘Using conjoint analysis to assess women’s preferences for miscarriage management’, Health Economics, 6(3): 261–273.3.0.CO;2-N>CrossRefGoogle Scholar
Ryan, M., Netten, A., Skatun, D. and Smith, P. (2006), ‘Using discrete choice experiments to estimate a preference-based measure of outcome: an application to social care for older people’, Journal of Health Economics, 25(5): 927–944.CrossRefGoogle ScholarPubMed
Ryan, M. and Skatun, D. (2004), ‘Modelling non-demanders in choice experiments’, Health Economics, 13(4): 397–402.CrossRefGoogle ScholarPubMed
Ryan, M. and Watson, V. (In press), ‘Comparing welfare estimates from payment card contingent valuation and discrete choice experiments’, Health Economics.Google Scholar
Scott, A. (2001), ‘Eliciting GPs’ preferences for pecuniary and non-pecuniary job characteristics’, Journal of Health Economics, 20: 329–347.CrossRefGoogle ScholarPubMed
Street, D. A. and Burgess, L. (2004), ‘Optimal and near-optimal pairs for the estimation of effects in 2-level choice experiments’, Journal of Statistical Planning and Inference, 118: 185–199.CrossRefGoogle Scholar
Street, D. A. and Burgess, L. (2007), The Construction of Optimal Stated Choice Experiments: Theory and Methods, Hoboken, NJ: Wiley.CrossRefGoogle Scholar
Street, D. A., Burgess, L. and Louviere, J. J. (2005), ‘Quick and easy choice sets: constructing optimal and nearly optimal stated choice experiments’, International Journal of Research in Marketing, 22(4): 459–470.CrossRefGoogle Scholar
Street, D. J., Burgess, L., Viney, R. and Louviere, J. J. (2008), ‘Designing Discrete Choice Experiments for Health Care’, in Ryan, M., Gerard, K.and Amaya-Amaya, M. (eds), Using Discrete Choice Experiments to Value Health and Health Care, Dordrecht, The Netherlands: Springer: 47–72.Google Scholar
Swait, J. (2001), ‘A non-compensatory choice model incorporating attribute cutoffs’, Transportation Research. Part B: Methodological, 35(10): 903–928.CrossRefGoogle Scholar
Swait, J. and Adamowicz, W. (2001), ‘Choice environment, market complexity, and consumer behavior: a theoretical and empirical approach for incorporating decision complexity into models of consumer choice’, Organizational Behavior and Human Decision Processes, 86(2): 141–167.CrossRefGoogle Scholar
Thurstone, L. (1927), ‘A law of comparative judgement’, Psychological Review, 34: 273–286.CrossRefGoogle Scholar
van der Pol, M. and Cairns, J. (2001), ‘Estimating time preference for health using discrete choice experiments’, Social Science and Medicine, 52: 1459–1470.CrossRefGoogle ScholarPubMed
Viney, R., Lancsar, E. and Louviere, J. (2002), ‘Discrete choice experiments to measure consumer preferences for health and healthcare’, Expert Review of Pharmacoeconomics and Outcomes Research, 2(4): 319–326.CrossRefGoogle ScholarPubMed
Viney, R., Savage, E. and Louviere, J. (2005), ‘Empirical investigation of experimental design properties of discrete choice experiments in health care’, Health Economics, 14(4): 349–362.CrossRefGoogle ScholarPubMed
Wells, A. J. (1991), ‘Optimal presentation orders for the method of paired comparisons’, British Journal of Mathematical and Statistical Psychology, 44(1): 181–193.CrossRefGoogle Scholar