, Volume 26, Issue 8, pp 661–677 | Cite as

Conducting Discrete Choice Experiments to Inform Healthcare Decision Making

A User’s Guide
  • Emily LancsarEmail author
  • Jordan Louviere
Practical Application


Discrete choice experiments (DCEs) are regularly used in health economics to elicit preferences for healthcare products and programmes. There is growing recognition that DCEs can provide more than information on preferences and, in particular, they have the potential to contribute more directly to outcome measurement for use in economic evaluation. Almost uniquely, DCEs could potentially contribute to outcome measurement for use in both cost-benefit and cost-utility analysis.

Within this expanding remit, our intention is to provide a resource for current practitioners as well as those considering undertaking a DCE, using DCE results in a policy/commercial context, or reviewing a DCE. We present the fundamental principles and theory underlying DCEs. To aid in undertaking and assessing the quality of DCEs, we discuss the process of carrying out a choice study and have developed a checklist covering conceptualizing the choice process, selecting attributes and levels, experimental design, questionnaire design, pilot testing, sampling and sample size, data collection, coding of data, econometric analysis, validity, interpretation and welfare and policy analysis.

In this fast-moving area, a number of issues remain on the research frontier. We therefore outline potentially fruitful areas for future research associated both with DCEs in general, and with health applications specifically, paying attention to how the results of DCEs can be used in economic evaluation. We also discuss emerging research trends.

We conclude that if appropriately designed, implemented, analysed and interpreted, DCEs offer several advantages in the health sector, the most important of which is that they provide rich data sources for economic evaluation and decision making, allowing investigation of many types of questions, some of which otherwise would be intractable analytically. Thus, they offer viable alternatives and complements to existing methods of valuation and preference elicitation.


Economic Evaluation Contingent Valuation Attribute Level Discrete Choice Experiment Conditional Logit Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



No sources of funding were used to assist in the preparation of this article. The authors have no conflicts of interest that are directly relevant to the content of this article.

The authors thank the anonymous referees for helpful comments.


  1. 1.
    Lancsar E. Deriving welfare measures from stated preference discrete choice modelling experiments [CHERE discussion paper no. 48]. Sydney: Centre for Health Economics Research and Evaluation, University of Technology Sydney, 2002Google Scholar
  2. 2.
    Drummond MF, Sculpher MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press, 2005Google Scholar
  3. 3.
    Krantz DH, Tversky A. Conjoint measurement analysis of composition rules in psychology. Psychology Rev 1971; 78: 151–169CrossRefGoogle Scholar
  4. 4.
    Luce RD, Tukey JW. Simultaneous conjoint measurement: a new type of fundamental measurement. J Math Psychol 1964; 1: 1–27CrossRefGoogle Scholar
  5. 5.
    Anderson NH. Foundations of information integration theory. New York: Academic Press, 1981Google Scholar
  6. 6.
    McFadden D. Conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Frontiers of econometrics. New York: Academic Press, 1974: 105–142Google Scholar
  7. 7.
    Bishop YM, Fienberg SW, Holland PW. Discrete multivariate analysis. Cambridge: MIT Press, 1975Google Scholar
  8. 8.
    Box GEP, Hunter WG, Hunter JS. Statistics for experimenters. New York: Wiley, 1978Google Scholar
  9. 9.
    Louviere J, Woodworm G. Design and analysis of simulated consumer choice or allocation experiments: an approach based on aggregated data. J Mark Res 1983; 20: 350–367CrossRefGoogle Scholar
  10. 10.
    Hensher DA, Louviere JJ. On the design and analysis of simulated choice or allocation experiments in travel choice modelling. Transport Res 1983; 890: 11–17Google Scholar
  11. 11.
    Adamowicz W, Louviere J, Williams M. Combining revealed and stated preference methods for valuing environmental amenities. J Environ Manage Econ 1994; 26 (3): 271–292CrossRefGoogle Scholar
  12. 12.
    Propper C. Contingent valuation of time spent on NHS waiting lists. Econ J 1990; 100: 193–199CrossRefGoogle Scholar
  13. 13.
    Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future research reflections. Applied Health Econ Health Policy 2003; 2 (1): 55–64Google Scholar
  14. 14.
    Ryan M, Gerard K, Amaya-Amaya M. Using discrete choice experiments to value health and health care. Dordrecht: Springer, 2008CrossRefGoogle Scholar
  15. 15.
    Bryan S, Dolan P. Discrete choice experiments in health economics: for better or for worse? Eur J Health Econ 2004; 5 (3): 199–202PubMedCrossRefGoogle Scholar
  16. 16.
    Wainright DM. More ‘con’ than ‘joint’: problems with the application of conjoint analysis to participatory healthcare decision making. Crit Public Health 2003; 13: 373–380CrossRefGoogle Scholar
  17. 17.
    Lancsar E, Donaldson C. Discrete choice experiments in health economics: distinguishing between the method and its application [comment]. Eur J Health Econ 2005; 6 (4): 314–316PubMedCrossRefGoogle Scholar
  18. 18.
    Torrance GW. Measurement of health state utilities for economic appraisal. J Health Econ 1986 5: 1–30PubMedCrossRefGoogle Scholar
  19. 19.
    Nord E, Pinto JL, Richardson J, et al. Incorporating societal concerns for fairness in numerical valuations of health programmes [published erratum appears in Health Econ 1999 Sep; 8 (6): 559]. Health Econ 1999; 8 (1): 25–39PubMedCrossRefGoogle Scholar
  20. 20.
    Bleichrodt H. QALYs and HYEs (healthy year equivalents): under what conditions are they equivalent? J Health Econ 1995; 14 (1): 17–37PubMedCrossRefGoogle Scholar
  21. 21.
    Pliskin J, Shepard D, Weinstein W. Utility functions for life years and health status. Oper Res 1980; 28: 206–224CrossRefGoogle Scholar
  22. 22.
    Johnson ER, Banzhaf MR, Desvousges WH. Willingness to pay for improved respiratory and cardiovascular health: a multiple-format, stated-preference approach. Health Econ 2000; 9 (4): 295–317PubMedCrossRefGoogle Scholar
  23. 23.
    Ryan M. Using conjoint analysis to take account of patient preferences and go beyond health outcomes: an application to in vitro fertilisation. Soc Sci Med 1999; 48 (4): 535–546PubMedCrossRefGoogle Scholar
  24. 24.
    Ryan M, Hughes J. Using conjoint analysis to assess women’s preferences for miscarriage management. Health Econ 1997; 6: 261–273PubMedCrossRefGoogle Scholar
  25. 25.
    Scott A. Eliciting GPs’ preferences for pecuniary and non-pecuniary job characteristics. J Health Econ 2001; 20: 329–347PubMedCrossRefGoogle Scholar
  26. 26.
    Chakraborty G, Ettensen R, Gaeth G. How consumers choose health insurance. J Health Care Mark 1994; 14 (1): 21–33PubMedGoogle Scholar
  27. 27.
    Jan S, Mooney G, Ryan M, et al. The use of conjoint analysis to elicit community preferences in public health research: a case study of hospital services in South Australia. Aust NZ J Public Health 2000; 24: 64–70CrossRefGoogle Scholar
  28. 28.
    Morgan A, Shackley P, Pickin M, et al. Quantifying patient preferences for out-of-hours primary care. J Health Serv Res Policy 2000; 5: 214–218PubMedGoogle Scholar
  29. 29.
    van der Pol M, Cairns J. Estimating time preference for health using discrete choice experiments. Soc Sci Med 2001; 52: 1459–1470PubMedCrossRefGoogle Scholar
  30. 30.
    Hall J, Kenny P, King M, et al. Using stated preference discrete choice modelling to evaluate the introduction of varicella vaccination. Health Econ 2002; 11: 457–465PubMedCrossRefGoogle Scholar
  31. 31.
    King MT, Hall J, Lancsar E, et al. Patient preferences for managing asthma: results from a discrete choice experiment. Health Econ 2007; 16 (7): 703–717PubMedCrossRefGoogle Scholar
  32. 32.
    Lancsar EJ, Hall JP, King M, et al. Using discrete choice experiments to investigate subject preferences for preventive asthma medication. Respirology 2007; 12 (1): 127–136PubMedCrossRefGoogle Scholar
  33. 33.
    Hakim Z, Pathak DS. Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling. Health Econ 1999; 8 (2): 103–116PubMedCrossRefGoogle Scholar
  34. 34.
    Sculpher M, Bryan S, Fry P, et al. Patients’ preferences for the management of non-metastatic prostate cancer: discrete choice experiment. BMJ 2004; 328: 382–384PubMedCrossRefGoogle Scholar
  35. 35.
    Lancsar E, Savage E. Deriving welfare measures from discrete choice experiments: inconsistency between current methods and random utility and welfare theory. Health Econ 2004; 13 (9): 901–907PubMedCrossRefGoogle Scholar
  36. 36.
    Mcintosh E. Using discrete choice experiments within a cost-benefit analysis framework: some considerations. Pharmacoeconomics 2006; 24 (9): 855–868PubMedCrossRefGoogle Scholar
  37. 37.
    Ryan M, Netten A, Skatun D, et al. Using discrete choice experiments to estimate a preference-based measure of outcome: an application to social care for older people. J Health Econ 2006; 25 (5): 927–944PubMedCrossRefGoogle Scholar
  38. 38.
    Viney R, Savage E, Louviere J. Empirical investigation of experimental design properties of discrete choice experiments in health care. Health Econ 2005; 14 (4): 349–362PubMedCrossRefGoogle Scholar
  39. 39.
    Lancaster K. A new approach to consumer theory. J Polit Econ 1966; 74: 132–157CrossRefGoogle Scholar
  40. 40.
    Hanley N, Mourato S, Wright RE. Choice modelling approaches: a superior alternative for environmental valuation? J Econ Surv 2001; 15: 435–462Google Scholar
  41. 41.
    Lancsar E, Louviere J. Deleting ‘irrational’ responses from discrete choice experiments: a case of investigating or imposing preferences? Health Econ 2006; 15 (8): 797–811PubMedCrossRefGoogle Scholar
  42. 42.
    Thurstone L. A law of comparative judgement. Psycholog Rev 1927; 34: 273–286CrossRefGoogle Scholar
  43. 43.
    Ben-Akiva M, Lerman SJ. Discrete choice analysis: theory and applications to travel demand. Cambridge: The MIT Press, 1985Google Scholar
  44. 44.
    Adamowicz W, Bunch D, Cameron T, et al. Behavioural frontiers in choice modelling. Marketing Lett. In pressGoogle Scholar
  45. 45.
    Train KE. Discrete choice methods with simulation. Cambridge: Cambridge University Press, 2003CrossRefGoogle Scholar
  46. 46.
    Swait J, Louviere J. The role of the scale parameter in the estimation and comparison of multinomial logit models. J Mark Res 1993; 30: 305–314CrossRefGoogle Scholar
  47. 47.
    Carson RT, Groves T, Machina MJ. Incentive and informational properties of preference questions. San Diego (CA): University of California, 2000Google Scholar
  48. 48.
    Louviere J, Lancsar E. Distinguishing between conjoint analysis and discrete choice experiments with implications for stated preference and welfare elicitation. Sydney (NSW): CenSoC, University of Technology, 2008Google Scholar
  49. 49.
    Ryan M, Skatun D. Modelling non-demanders in choice experiments. Health Econ 2004; 13 (4): 397–402PubMedCrossRefGoogle Scholar
  50. 50.
    Viney R, Lancsar E, Louviere J. Discrete choice experiments to measure consumer preferences for health and healthcare. Exp Rev Pharmacoeconomics Outcomes Res 2002 August; 2 (4): 319–326CrossRefGoogle Scholar
  51. 51.
    Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. Econometrica 1979; 47: 263–291CrossRefGoogle Scholar
  52. 52.
    Coast J, Horrocks S. Developing attributes and levels for discrete choice experiments using qualitative methods. J Health Serv Res Pol 2007; 12 (1): 25–30CrossRefGoogle Scholar
  53. 53.
    Slothuus Skjoldborg U, Gyrd-Hansen D. Conjoint analysis: the cost variable. An Achilles’ heel? Health Econ 2003; 12 (6): 479–491CrossRefGoogle Scholar
  54. 54.
    Hanley N, Adamowicz W, Wright RE. Price vector effects in choice experiments: an empirical test. Res Energy Econ 2005; 27: 227–234CrossRefGoogle Scholar
  55. 55.
    Smith R. Construction of the contingent valuation market in health care: a critical assessment. Health Econ 2003; 12: 609–628PubMedCrossRefGoogle Scholar
  56. 56.
    Peters E, Vastfjall D, Slovic P, et al. Numeracy and decision making. Psycholog Sci 2006; 17 (5): 407–413CrossRefGoogle Scholar
  57. 57.
    Hall J, Fiebig DG, King MT, et al. What influences participation in genetic carrier testing? Results from a discrete choice experiment. J Health Econ 2006; 25 (3): 520–537PubMedCrossRefGoogle Scholar
  58. 58.
    Lusk JL, Norwood FB. Effect of experimental design on choice-based conjoint valuation estimates. Am J Agric Econ 2005; 87 (3): 771–785CrossRefGoogle Scholar
  59. 59.
    Louviere JJ, Wasi N. A warning about possibly misleading conclusions in Lusk and Norwood (2005) [CenSoC working paper]. Sydney (NSW): University of Technology, Sydney, 2008Google Scholar
  60. 60.
    Street DA, Burgess L, Louviere JJ. Quick and easy choice sets: constructing optimal and nearly optimal stated choice experiments. Int J Res Mark 2005; 22: 459–470CrossRefGoogle Scholar
  61. 61.
    Street DA, Burgess L. The construction of optimal stated choice experiments: theory and methods. Hoboken (NJ): Wiley, 2007CrossRefGoogle Scholar
  62. 62.
    Louviere JJ, Hensher DA, Swait JD. Stated choice methods analysis and application. Cambridge: Cambridge University Press, 2000CrossRefGoogle Scholar
  63. 63.
    Huber J, Zwerina K. The importance of utility balance in efficient choice designs. J Mark Res 1996; 33: 307–317CrossRefGoogle Scholar
  64. 64.
    Louviere J, Engle T. Confound it! That pesky little scale constant messes up our convenient assumptions. 2006 Sawtooth Software Conference Proceedings; 2006 Mar 29–31; Delray Beach (FL). Sequim (WA): Sawtooth Software: 211-28Google Scholar
  65. 65.
    Louviere JJ, Meyer RJ. Formal choice models of informal choices: what choice modeling research can (and can’t) learn from behavioural theory. Rev Mark Res 2007; 4: 3–32Google Scholar
  66. 66.
    DeShazo JR, Fermo G. Designing choice sets for stated preference methods: the effects of complexity on choice consistency. J Environ Econ Manage 2002; 44: 123–143CrossRefGoogle Scholar
  67. 67.
    Louviere J, Islam T, Wasi N, et al. Designing discrete choice experiments: do optimal designs come at a price? J Consumer Res 2008 Aug. In pressGoogle Scholar
  68. 68.
    San Miguel F, Ryan M, Amaya-Amaya M. Irrational stated preferences: a quantitative and qualitative investigation. Health Econ 2004; 14: 307–322Google Scholar
  69. 69.
    Shackley P, Donaldson C. Willingness to pay for publicly-financed health care: how should we use the numbers? App Econ 2000; 32: 2015–2021CrossRefGoogle Scholar
  70. 70.
    Dillman DA, Bowker DK. Mail and internet surveys: the tailored design method. New York: Wiley, 2001Google Scholar
  71. 71.
    Bech M, Gyrd-Hansen D. Effects coding in discrete choice experiments. Health Econ 2005; 14 (10): 1079–1083PubMedCrossRefGoogle Scholar
  72. 72.
    Kjaer T, Gyrd-Hansen D. Preference heterogeneity and choice of cardiac rehabilitation program: results from a discrete choice experiment. Health Policy 2008; 85: 124–132PubMedCrossRefGoogle Scholar
  73. 73.
    McFadden D, Train KE. Mixed MNL models for discrete response. J Applied Econometrics 2000; 15: 447–470CrossRefGoogle Scholar
  74. 74.
    Swait J, Adamowicz W. The influence of task complexity on consumer choice: a latent class model of decision strategy switching. J Con Res 2001; 28 (1): 135–148CrossRefGoogle Scholar
  75. 75.
    Islam T, Louviere JJ, Burke PF. Modeling the effects of including/excluding attributes in choice experiment on systematic and random components. Int J Res Mark 2007; 24: 289–300CrossRefGoogle Scholar
  76. 76.
    Swait J, Adamowicz W. Choice environment, market complexity, and consumer behavior: a theoretical and empirical approach for incorporating decision complexity into models of consumer choice. Organ Behav Human Decision Processes 2001; 86 (2): 141–167CrossRefGoogle Scholar
  77. 77.
    Magidson J, Vermunt J. Removing the scale factor confound in multinomial logit choice models to obtain better estimates of preference. 2007 Sawtooth Software Conference; 2007 Oct 17–19; Santa Rosa (CA)Google Scholar
  78. 78.
    Lancsar E, Louviere J, Flynn T. Several methods to investigate relative attribute impact in stated preference experiments. Soc Sci Med 2007; 64 (8): 1738–1753PubMedCrossRefGoogle Scholar
  79. 79.
    Adamowicz W, Swait J, Boxall P, et al. Perceptions versus objective measures of environmental quality in combined revealed and stated preference models of environmental valuation. J Environ Manage 1997; 32: 65–84Google Scholar
  80. 80.
    Mark T, Swait J. Using stated preference and revealed preference modelling to evaluate prescribing decisions. Health Econ 2004; 13 (6): 563–573PubMedCrossRefGoogle Scholar
  81. 81.
    Lloyd AJ. Threats to the estimation of benefit: are preference elicitation methods accurate? Health Econ 2003; 12: 393–402PubMedCrossRefGoogle Scholar
  82. 82.
    Mcintosh E, Ryan M. Using discrete choice experiments to derive welfare estimates for the provision of elective surgery: implications of discontinuous preferences. J Econ Psychol 2002; 23 (3): 367–382CrossRefGoogle Scholar
  83. 83.
    Ryan M, San Miguel F. Revisiting the axiom of completeness in health care. Health Econ 2003; 12 (4): 295–307PubMedCrossRefGoogle Scholar
  84. 84.
    Gyrd-Hansen D, Søgaard J. Analysing public preferences for cancer screening programmes. Health Econ 2001; 10 (7): 617–634PubMedCrossRefGoogle Scholar
  85. 85.
    Small KA, Rosen HS. Applied welfare economics with discrete choice models. Econometrica 1981; 49 (1): 105–130CrossRefGoogle Scholar
  86. 86.
    Baker R, Donaldson C, Lancsar E, et al. Deriving QALY weights through discrete choice experiments: challenges and preliminary results.Health Economics Study Group Meeting; 2008 Jan 9–11; NorwichGoogle Scholar
  87. 87.
    Risa Hole A. A comparison of approaches to estimating confidence intervals for willingness to pay measures. Health Econ 2007; 16: 827–840CrossRefGoogle Scholar
  88. 88.
    Maddala T, Phillips KA, Reed Johnson F. An experiment on simplifying conjoint analysis designs for measuring preferences. Health Econ 2003; 12 (12): 1035–1047PubMedCrossRefGoogle Scholar
  89. 89.
    Severin V. Comparing statistical efficiency and respondent efficiency in choice experiments. Sydney (NSW): University of Sydney, 2000Google Scholar
  90. 90.
    Bryan S, Gill P, Greenfield S, et al. The myth of agency and patient choice in health care? The case of dmg treatments to prevent coronary disease. Soc Sci Med 2006; 63 (10): 2698–2701PubMedCrossRefGoogle Scholar
  91. 91.
    Vick S, Scott A. Agency in health care: examining patients’ preferences for attributes of the doctor-patient relationship. J Health Econ 1998; 17: 587–605PubMedCrossRefGoogle Scholar
  92. 92.
    Battels R, Fiebig DG, van Soest A. Consumers and experts: an econometric analysis of the demand for water heaters. Empirical Econ 2006; 31: 639–391Google Scholar
  93. 93.
    Louviere J, Street D, Carson R, et al. Dissecting the random component of utility. Marketing Lett 2002; 13 (3): 177–193CrossRefGoogle Scholar
  94. 94.
    Brouwer R, Bateman IJ. Benefit transfer of willingness to pay estimates and functions for health-risk reductions: a crosscountry study. J Health Econ 2005; 24: 591–611PubMedCrossRefGoogle Scholar
  95. 95.
    Ryan M, Bate A. Testing the assumptions of rationality, continuity and symmetry when applyling discrete choice experiments in health care. Appl Econ Lett 2001; 8: 59–63CrossRefGoogle Scholar
  96. 96.
    Swait J. A non-compensatory choice model incorporating attribute cutoffs. Transportation Research B 2001; 35 (10): 903–928CrossRefGoogle Scholar
  97. 97.
    Marley A, Louviere J. Some probabilistic models of best, worst, and best-worst choices. J Math Psychol 2005; 49 (6): 464–480CrossRefGoogle Scholar
  98. 98.
    Marley AAJ, Louviere JJ, Flynn T. Probabilistic models of set-dependent and attribute-level best-worst choice. J Math Psychol. In pressGoogle Scholar
  99. 99.
    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): 171–189PubMedCrossRefGoogle Scholar
  100. 100.
    Mcintosh E, Louviere J. Separating weight and scale value: an exploration of best-attribute scaling in health economics. Health Economics Study Group Meeting; 2002 Jul 3–5; LondonGoogle Scholar
  101. 101.
    Lancsar E, Louviere J. Several methods for dealing with scale confound and efficiency in stated preference data with an empirical illustration.Health Economics Study Group Meeting; 2005 Jun 29–Jul 1; Newcastle upon TyneGoogle Scholar
  102. 102.
    Anand P. QALYs and the integration of claims in health-care rationing. Health Care Anal 1999; 7: 239–253PubMedCrossRefGoogle Scholar
  103. 103.
    Longo MF, Cohen DR, Hood K, et al. Involving patients in primary care consultations: assessing preferences using discrete choice experiments. Br J Gen Practice 2006; 56: 35–42Google Scholar
  104. 104.
    Rateliffe J. Public preferences for the allocation of donor liver grafts for transplantation. Health Econ 2000; 9 (2): 137–148CrossRefGoogle Scholar
  105. 105.
    Ruta D, Mitton C, Bate A, et al. Programme budgeting and marginal analysis: bridging the divide between doctors and managers. BMJ 2005; 330: 1501–1503PubMedCrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Business School (Economics) and Institute of Health and SocietyUniversity of Newcastle upon TyneNewcastle upon TyneUK
  2. 2.Centre for the Study of ChoiceUniversity of TechnologySydneyAustralia

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