Top

PharmacoEconomics

Published in:

01-08-2008 | Practical Application

Conducting Discrete Choice Experiments to Inform Healthcare Decision Making

A User’s Guide

Authors: Emily Lancsar, Jordan Louviere

Published in: PharmacoEconomics | Issue 8/2008

Abstract

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.

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, 2002

Drummond MF, Sculpher MJ, Torrance GW, et al. Methods for the economic evaluation of health care programmes. 3rd ed. Oxford: Oxford University Press, 2005

Krantz DH, Tversky A. Conjoint measurement analysis of composition rules in psychology. Psychology Rev 1971; 78: 151–169CrossRef

Luce RD, Tukey JW. Simultaneous conjoint measurement: a new type of fundamental measurement. J Math Psychol 1964; 1: 1–27CrossRef

Anderson NH. Foundations of information integration theory. New York: Academic Press, 1981

McFadden D. Conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Frontiers of econometrics. New York: Academic Press, 1974: 105–142

Bishop YM, Fienberg SW, Holland PW. Discrete multivariate analysis. Cambridge: MIT Press, 1975

Box GEP, Hunter WG, Hunter JS. Statistics for experimenters. New York: Wiley, 1978

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: 350–367CrossRef

10.

Hensher DA, Louviere JJ. On the design and analysis of simulated choice or allocation experiments in travel choice modelling. Transport Res 1983; 890: 11–17

11.

Adamowicz W, Louviere J, Williams M. Combining revealed and stated preference methods for valuing environmental amenities. J Environ Manage Econ 1994; 26 (3): 271–292CrossRef

12.

Propper C. Contingent valuation of time spent on NHS waiting lists. Econ J 1990; 100: 193–199CrossRef

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): 55–64

14.

Ryan M, Gerard K, Amaya-Amaya M. Using discrete choice experiments to value health and health care. Dordrecht: Springer, 2008CrossRef

15.

Bryan S, Dolan P. Discrete choice experiments in health economics: for better or for worse? Eur J Health Econ 2004; 5 (3): 199–202PubMedCrossRef

16.

Wainright DM. More ‘con’ than ‘joint’: problems with the application of conjoint analysis to participatory healthcare decision making. Crit Public Health 2003; 13: 373–380CrossRef

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): 314–316PubMedCrossRef

18.

Torrance GW. Measurement of health state utilities for economic appraisal. J Health Econ 1986 5: 1–30PubMedCrossRef

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): 25–39PubMedCrossRef

20.

Bleichrodt H. QALYs and HYEs (healthy year equivalents): under what conditions are they equivalent? J Health Econ 1995; 14 (1): 17–37PubMedCrossRef

21.

Pliskin J, Shepard D, Weinstein W. Utility functions for life years and health status. Oper Res 1980; 28: 206–224CrossRef

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): 295–317PubMedCrossRef

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): 535–546PubMedCrossRef

24.

Ryan M, Hughes J. Using conjoint analysis to assess women’s preferences for miscarriage management. Health Econ 1997; 6: 261–273PubMedCrossRef

25.

Scott A. Eliciting GPs’ preferences for pecuniary and non-pecuniary job characteristics. J Health Econ 2001; 20: 329–347PubMedCrossRef

26.

Chakraborty G, Ettensen R, Gaeth G. How consumers choose health insurance. J Health Care Mark 1994; 14 (1): 21–33PubMed

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: 64–70CrossRef

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: 214–218PubMed

29.

van der Pol M, Cairns J. Estimating time preference for health using discrete choice experiments. Soc Sci Med 2001; 52: 1459–1470PubMedCrossRef

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: 457–465PubMedCrossRef

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): 703–717PubMedCrossRef

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): 127–136PubMedCrossRef

33.

Hakim Z, Pathak DS. Modelling the EuroQol data: a comparison of discrete choice conjoint and conditional preference modelling. Health Econ 1999; 8 (2): 103–116PubMedCrossRef

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: 382–384PubMedCrossRef

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): 901–907PubMedCrossRef

36.

Mcintosh E. Using discrete choice experiments within a cost-benefit analysis framework: some considerations. Pharmacoeconomics 2006; 24 (9): 855–868PubMedCrossRef

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): 927–944PubMedCrossRef

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): 349–362PubMedCrossRef

39.

Lancaster K. A new approach to consumer theory. J Polit Econ 1966; 74: 132–157CrossRef

40.

Hanley N, Mourato S, Wright RE. Choice modelling approaches: a superior alternative for environmental valuation? J Econ Surv 2001; 15: 435–462

41.

Lancsar E, Louviere J. Deleting ‘irrational’ responses from discrete choice experiments: a case of investigating or imposing preferences? Health Econ 2006; 15 (8): 797–811PubMedCrossRef

42.

Thurstone L. A law of comparative judgement. Psycholog Rev 1927; 34: 273–286CrossRef

43.

Ben-Akiva M, Lerman SJ. Discrete choice analysis: theory and applications to travel demand. Cambridge: The MIT Press, 1985

44.

Adamowicz W, Bunch D, Cameron T, et al. Behavioural frontiers in choice modelling. Marketing Lett. In press

45.

Train KE. Discrete choice methods with simulation. Cambridge: Cambridge University Press, 2003CrossRef

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: 305–314CrossRef

47.

Carson RT, Groves T, Machina MJ. Incentive and informational properties of preference questions. San Diego (CA): University of California, 2000

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, 2008

49.

Ryan M, Skatun D. Modelling non-demanders in choice experiments. Health Econ 2004; 13 (4): 397–402PubMedCrossRef

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): 319–326CrossRef

51.

Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. Econometrica 1979; 47: 263–291CrossRef

52.

Coast J, Horrocks S. Developing attributes and levels for discrete choice experiments using qualitative methods. J Health Serv Res Pol 2007; 12 (1): 25–30CrossRef

53.

Slothuus Skjoldborg U, Gyrd-Hansen D. Conjoint analysis: the cost variable. An Achilles’ heel? Health Econ 2003; 12 (6): 479–491CrossRef

54.

Hanley N, Adamowicz W, Wright RE. Price vector effects in choice experiments: an empirical test. Res Energy Econ 2005; 27: 227–234CrossRef

55.

Smith R. Construction of the contingent valuation market in health care: a critical assessment. Health Econ 2003; 12: 609–628PubMedCrossRef

56.

Peters E, Vastfjall D, Slovic P, et al. Numeracy and decision making. Psycholog Sci 2006; 17 (5): 407–413CrossRef

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): 520–537PubMedCrossRef

58.

Lusk JL, Norwood FB. Effect of experimental design on choice-based conjoint valuation estimates. Am J Agric Econ 2005; 87 (3): 771–785CrossRef

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, 2008

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: 459–470CrossRef

61.

Street DA, Burgess L. The construction of optimal stated choice experiments: theory and methods. Hoboken (NJ): Wiley, 2007CrossRef

62.

Louviere JJ, Hensher DA, Swait JD. Stated choice methods analysis and application. Cambridge: Cambridge University Press, 2000CrossRef

63.

Huber J, Zwerina K. The importance of utility balance in efficient choice designs. J Mark Res 1996; 33: 307–317CrossRef

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: 211-28

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: 3–32

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: 123–143CrossRef

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 press

68.

San Miguel F, Ryan M, Amaya-Amaya M. Irrational stated preferences: a quantitative and qualitative investigation. Health Econ 2004; 14: 307–322

69.

Shackley P, Donaldson C. Willingness to pay for publicly-financed health care: how should we use the numbers? App Econ 2000; 32: 2015–2021CrossRef

70.

Dillman DA, Bowker DK. Mail and internet surveys: the tailored design method. New York: Wiley, 2001

71.

Bech M, Gyrd-Hansen D. Effects coding in discrete choice experiments. Health Econ 2005; 14 (10): 1079–1083PubMedCrossRef

72.

Kjaer T, Gyrd-Hansen D. Preference heterogeneity and choice of cardiac rehabilitation program: results from a discrete choice experiment. Health Policy 2008; 85: 124–132PubMedCrossRef

73.

McFadden D, Train KE. Mixed MNL models for discrete response. J Applied Econometrics 2000; 15: 447–470CrossRef

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): 135–148CrossRef

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: 289–300CrossRef

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): 141–167CrossRef

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)

78.

Lancsar E, Louviere J, Flynn T. Several methods to investigate relative attribute impact in stated preference experiments. Soc Sci Med 2007; 64 (8): 1738–1753PubMedCrossRef

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: 65–84

80.

Mark T, Swait J. Using stated preference and revealed preference modelling to evaluate prescribing decisions. Health Econ 2004; 13 (6): 563–573PubMedCrossRef

81.

Lloyd AJ. Threats to the estimation of benefit: are preference elicitation methods accurate? Health Econ 2003; 12: 393–402PubMedCrossRef

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): 367–382CrossRef

83.

Ryan M, San Miguel F. Revisiting the axiom of completeness in health care. Health Econ 2003; 12 (4): 295–307PubMedCrossRef

84.

Gyrd-Hansen D, Søgaard J. Analysing public preferences for cancer screening programmes. Health Econ 2001; 10 (7): 617–634PubMedCrossRef

85.

Small KA, Rosen HS. Applied welfare economics with discrete choice models. Econometrica 1981; 49 (1): 105–130CrossRef

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; Norwich

87.

Risa Hole A. A comparison of approaches to estimating confidence intervals for willingness to pay measures. Health Econ 2007; 16: 827–840CrossRef

88.

Maddala T, Phillips KA, Reed Johnson F. An experiment on simplifying conjoint analysis designs for measuring preferences. Health Econ 2003; 12 (12): 1035–1047PubMedCrossRef

89.

Severin V. Comparing statistical efficiency and respondent efficiency in choice experiments. Sydney (NSW): University of Sydney, 2000

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): 2698–2701PubMedCrossRef

91.

Vick S, Scott A. Agency in health care: examining patients’ preferences for attributes of the doctor-patient relationship. J Health Econ 1998; 17: 587–605PubMedCrossRef

92.

Battels R, Fiebig DG, van Soest A. Consumers and experts: an econometric analysis of the demand for water heaters. Empirical Econ 2006; 31: 639–391

93.

Louviere J, Street D, Carson R, et al. Dissecting the random component of utility. Marketing Lett 2002; 13 (3): 177–193CrossRef

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: 591–611PubMedCrossRef

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: 59–63CrossRef

96.

Swait J. A non-compensatory choice model incorporating attribute cutoffs. Transportation Research B 2001; 35 (10): 903–928CrossRef

97.

Marley A, Louviere J. Some probabilistic models of best, worst, and best-worst choices. J Math Psychol 2005; 49 (6): 464–480CrossRef

98.

Marley AAJ, Louviere JJ, Flynn T. Probabilistic models of set-dependent and attribute-level best-worst choice. J Math Psychol. In press

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): 171–189PubMedCrossRef

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; London

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 Tyne

102.

Anand P. QALYs and the integration of claims in health-care rationing. Health Care Anal 1999; 7: 239–253PubMedCrossRef

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: 35–42

104.

Rateliffe J. Public preferences for the allocation of donor liver grafts for transplantation. Health Econ 2000; 9 (2): 137–148CrossRef

105.

Ruta D, Mitton C, Bate A, et al. Programme budgeting and marginal analysis: bridging the divide between doctors and managers. BMJ 2005; 330: 1501–1503PubMedCrossRef

Title: Conducting Discrete Choice Experiments to Inform Healthcare Decision Making
A User’s Guide
Authors: Emily Lancsar
Jordan Louviere
Publication date: 01-08-2008
Publisher: Springer International Publishing
Published in: PharmacoEconomics / Issue 8/2008
Print ISSN: 1170-7690
Electronic ISSN: 1179-2027
DOI: https://doi.org/10.2165/00019053-200826080-00004

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Conducting Discrete Choice Experiments to Inform Healthcare Decision Making

A User’s Guide

Abstract

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Please log in to get access to this content

Other articles of this Issue 8/2008

Trastuzumab

Waiting for Reimbursement of New Medicines in Canada

Modelling Approaches

Assessing the Impact of Global Price Interdependencies

Demand Forecasting for Preventive AIDS Vaccines