Skip to main content
Log in

Discrete Choice Experiments in Health Economics: A Review of the Literature

  • Systematic Review
  • Published:
PharmacoEconomics Aims and scope Submit manuscript

Abstract

Background

Discrete choice experiments (DCEs) are increasingly used in health economics to address a wide range of health policy-related concerns.

Objective

Broadly adopting the methodology of an earlier systematic review of health-related DCEs, which covered the period 2001–2008, we report whether earlier trends continued during 2009–2012.

Methods

This paper systematically reviews health-related DCEs published between 2009 and 2012, using the same database as the earlier published review (PubMed) to obtain citations, and the same range of search terms.

Results

A total of 179 health-related DCEs for 2009–2012 met the inclusion criteria for the review. We found a continuing trend towards conducting DCEs across a broader range of countries. However, the trend towards including fewer attributes was reversed, whilst the trend towards interview-based DCEs reversed because of increased computer administration. The trend towards using more flexible econometric models, including mixed logit and latent class, has also continued. Reporting of monetary values has fallen compared with earlier periods, but the proportion of studies estimating trade-offs between health outcomes and experience factors, or valuing outcomes in terms of utility scores, has increased, although use of odds ratios and probabilities has declined. The reassuring trend towards the use of more flexible and appropriate DCE designs and econometric methods has been reinforced by the increased use of qualitative methods to inform DCE processes and results. However, qualitative research methods are being used less often to inform attribute selection, which may make DCEs more susceptible to omitted variable bias if the decision framework is not known prior to the research project.

Conclusions

The use of DCEs in healthcare continues to grow dramatically, as does the scope of applications across an expanding range of countries. There is increasing evidence that more sophisticated approaches to DCE design and analytical techniques are improving the quality of final outputs. That said, recent evidence that the use of qualitative methods to inform attribute selection has declined is of concern.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Notes

  1. Lower income countries in 2008–2012 included Kenya, South Africa, Thailand, China, Ghana, Vietnam, Ethiopia, Peru, Ukraine, India, Cuba, Nepal, Turkey, and Burkina Faso.

  2. ‘Other’ packages used included Gauss for two analyses; nGene (a Bayesian efficient design) for four analyses; and the statistical design procedure Gosset for one analysis; a D-efficient design advocated by Rose and Bliemer for one analysis; STATA for one design; a design described as “an experimental design algorithm optimizing orthogonality, attribute balance, and efficiency” for one design; and Street and Burgess Software for one design.

  3. ‘Other’ methods used in 2009–2012 included weighted probit [68]; OLS with a hetero-robust covariance matrix estimator [192]; a method described as “modelling including interaction effects” [45]; Cox’s proportional hazards model with time-dependent covariate [105]; weighted least squares regression to estimate utility weights [105]; multivariate ordered probit to estimate conjoint utility parameters [76]; mixed logit with hierarchical Bayesian modeling and ordered probit [115]; generalized estimated equations [109, 125]; random parameter logit estimated using a hierarchical Bayesian algorithim [208]; conditional logit and parameter weighting functions [160]; a series of multivariate regressions [50, 65]; a method described as Bayesian-like for preference weights [80]; OLS [87]; hierarchical Bayesian analysis [48, 70, 114, 205, 212]; multinomial exploded logit [177]; Firth’s unbiased estimator [193]; combined conditional logit and ranked logit model [127]; multivariate multilevel logistic regression [46]; generalized multinomial logit [119]; mixed effect logistic regression [184], error components mixed logit analysis [63]; a combination of Bayes theorem, Monte Carlo Markov chain procedure and the Metropolis Hastings algorithm [182]; and logistic and probit regression using cluster-robust standard error (SE), random effects and GEE and multinomial logistic and probit regressions with cluster-robust SE and random effects multinomial logistic model and probit model with cluster-robust SE treating the choices from two stages as two correlated binary outcomes [94].

  4. In 2009–2012, one study explored how changing the number of responses elicited from respondents might affect estimates of WTP [204]; another looked at parents’ preferences for management of attention-deficit hyperactivity disorder [206]; one study looked at general public preferences for long-term care [137]; another two studies looked at preferences for human papillomavirus vaccine, one case looking a societal preferences [207] and the other [63] looking at mothers’ preferences; another study looked at the valuation of diagnostic testing for idiopathic developmental disability by the general population [208]; another looked at various stakeholder groups’ preferences for coagulation factor concentrates to treat hemophilia [145]; one study looked at general public preferences for tele-endocopy services [158]; another compared Dutch and German preferences for health insurance amongst their populations [214]; one paper looked at public and decision maker preferences for pharmaceutical subsidy decisions [215]; one study explored how individuals perceive various coronary heart disease factors [203], whilst another described the relative importance of major adverse cardiac and cerebrovascular events to be used when analyzing trials [212]. Two other DCEs were performed on the area of quality improvement; one investigated how to best disseminate evidence-based practices to addiction service providers and administrators [205], while the other was used to investigate which indicators had the greatest impact on the decisions of health service inspectors concerning the assessment of quality of mental health care [211]. Other applications included a study on preferences of health workers in Burkina Faso for health-insurance payment mechanisms [209]; a study on how respondents valued mortality risk attributable to climate change reductions [210]; and a study on the preferences for reducing contaminated sites to reduce the risk for cancer [213].

References

  1. Lancaster K. New approach to consumer theory. J Polit Econ. 1966;74(2):132–57.

    Google Scholar 

  2. McFadden D. Computing willingness-to-pay in random utility models. Trade theory and econometrics, chap. 15. In: Essays in honour of John S. Chipman. Studies in the Modern World Economy; 1999. p. 253–74.

  3. 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.

    PubMed  Google Scholar 

  4. 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.

    PubMed  Google Scholar 

  5. de Bekker-Grob EW, Chorus CG. Random regret-based discrete-choice modelling: an application to healthcare. Pharmacoeconomics. 2013;31(7):623–34.

    PubMed  Google Scholar 

  6. Petrou S, McIntosh E. Commentary: Using stated preference discrete choice experiments to elicit women’s preferences for aspects of maternity care. Birth. 2011;38(1):47–8.

    PubMed  Google Scholar 

  7. Lagarde M, Blaauw D. A review of the application and contribution of discrete choice experiments to inform human resources policy interventions. Hum Resour Health. 2009;7:62.

    PubMed Central  PubMed  Google Scholar 

  8. Clark MD, et al. ‘A better way to measure choices’, discrete choice experiment/conjoint analysis studies in Nephrology—a literature review. Eur Med J Nephrol. 2013;1:52–9.

    Google Scholar 

  9. Marshall D, et al. Conjoint analysis applications in health—how are studies being designed and reported? An update on current practice in the published literature between 2005 and 2008. Patient. 2010;3(4):249–56.

    PubMed  Google Scholar 

  10. Anderson JL, et al. 2011 ACCF/AHA focused update incorporated into the ACC/AHA 2007 guidelines for the management of patients with unstable angina/non-ST-elevation myocardial infarction: a report of the American College of Cardiology Foundation/American Heart Association Task Force on Practice Guidelines. Circulation. 2011;123(18):e426–579.

    PubMed  Google Scholar 

  11. Reed Johnson F, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. Value Health. 2013;16(1):3–13.

    CAS  PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  13. Fraenkel L. Conjoint analysis at the individual patient level: issues to consider as we move from a research to a clinical tool. Patient. 2008;1(4):251–3.

    PubMed Central  PubMed  Google Scholar 

  14. Lancsar E, Louviere J. Conducting discrete choice experiments to inform healthcare decision making: a user’s guide. Pharmacoeconomics. 2008;26(8):661–77.

    PubMed  Google Scholar 

  15. Coast J, et al. Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ. 2012;21(6):730–41.

    PubMed  Google Scholar 

  16. Bridges JF, 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.

    PubMed  Google Scholar 

  17. Flynn TN. Valuing citizen and patient preferences in health: recent developments in three types of best–worst scaling. Expert Rev Pharmacoecon Outcomes Res. 2010;10(3):259–67.

    PubMed  Google Scholar 

  18. Lancsar E, et al. Best worst discrete choice experiments in health: methods and an application. Soc Sci Med. 2013;76(1):74–82.

    PubMed  Google Scholar 

  19. Bell RA, et al. Encouraging patients with depressive symptoms to seek care: a mixed methods approach to message development. Patient Educ Couns. 2010;78(2):198–205.

    PubMed Central  PubMed  Google Scholar 

  20. de Achaval S, et al. Impact of educational and patient decision aids on decisional conflict associated with total knee arthroplasty. Arthritis Care Res (Hoboken). 2012;64(2):229–37.

    Google Scholar 

  21. Fraenkel L, et al. Patients’ preferences for treatment of hepatitis C. Med Decis Making. 2010;30(1):45–57.

    PubMed Central  PubMed  Google Scholar 

  22. Fraenkel L. Feasibility of using modified adaptive conjoint analysis importance questions. Patient. 2010;3(4):209–15.

    PubMed Central  PubMed  Google Scholar 

  23. Gregorian RS Jr, et al. Importance of side effects in opioid treatment: a trade-off analysis with patients and physicians. J Pain. 2010;11(11):1095–108.

    PubMed  Google Scholar 

  24. de Groot IB, et al. Is the impact of hospital performance data greater in patients who have compared hospitals? BMC Health Serv Res. 2011;11:214.

    PubMed Central  PubMed  Google Scholar 

  25. de Groot IB, et al. Choosing between hospitals: the influence of the experiences of other patients. Med Decis Making. 2012;32(6):764–78.

    PubMed  Google Scholar 

  26. Meister H, et al. Utility and importance of hearing-aid features assessed by hearing-aid acousticians. Trends Amplif. 2010;14(3):155–63.

    PubMed  Google Scholar 

  27. Pieterse AH, et al. Adaptive conjoint analysis as individual preference assessment tool: feasibility through the internet and reliability of preferences. Patient Educ Couns. 2010;78(2):224–33.

    PubMed  Google Scholar 

  28. Pieterse AH, Stiggelbout AM, Marijnen CA. Methodologic evaluation of adaptive conjoint analysis to assess patient preferences: an application in oncology. Health Expect. 2010;13(4):392–405.

    PubMed  Google Scholar 

  29. Rochon D, et al. Elderly patients’ experiences using adaptive conjoint analysis software as a decision aid for osteoarthritis of the knee. Health Expect. 2012.

  30. Halme M, Linden K, Kaaria K. Patients’ preferences for generic and branded over-the-counter medicines: an adaptive conjoint analysis approach. Patient. 2009;2(4):243–55.

    PubMed  Google Scholar 

  31. Ahmed SF, Smith WA, Blamires C. Facilitating and understanding the family’s choice of injection device for growth hormone therapy by using conjoint analysis. Arch Dis Child. 2008;93(2):110–4.

    CAS  PubMed  Google Scholar 

  32. Beusterien KM, et al. Understanding patient preferences for HIV medications using adaptive conjoint analysis: feasibility assessment. Value Health. 2005;8(4):453–61.

    PubMed  Google Scholar 

  33. Beusterien KM, et al. Patient preferences among third agent HIV medications: a US and German perspective. AIDS Care. 2007;19(8):982–8.

    CAS  PubMed  Google Scholar 

  34. Gan TJ, et al. Patient preferences for acute pain treatment. Br J Anaesth. 2004;92(5):681–8.

    CAS  PubMed  Google Scholar 

  35. Fraenkel L, Bodardus S, Wittnik DR. Understanding patient preferences for the treatment of lupus nephritis with adaptive conjoint analysis. Med Care. 2001;39(11):1203–16.

    CAS  PubMed  Google Scholar 

  36. Fraenkel L, Bogardus ST Jr, Wittink DR. Risk-attitude and patient treatment preferences. Lupus. 2003;12(5):370–6.

    CAS  PubMed  Google Scholar 

  37. Fraenkel L, et al. Informed choice and the widespread use of antiinflammatory drugs. Arthritis Rheum. 2004;51(2):210–4.

    PubMed  Google Scholar 

  38. Fraenkel L, et al. Patient preferences for treatment of rheumatoid arthritis. Ann Rheum Dis. 2004;63(11):1372–8.

    CAS  PubMed Central  PubMed  Google Scholar 

  39. Fraenkel L, et al. Are preferences for cyclooxygenase-2 inhibitors influenced by the certainty effect? J Rheumatol. 2004;31(3):591–3.

    CAS  PubMed  Google Scholar 

  40. Fraenkel L, Gulanski B, Wittink D. Patient treatment preferences for osteoporosis. Arthritis Rheum. 2006;55(5):729–35.

    PubMed Central  PubMed  Google Scholar 

  41. Fraenkel L, Gulanski B, Wittink D. Patient willingness to take teriparatide. Patient Educ Couns. 2007;65(2):237–44.

    PubMed Central  PubMed  Google Scholar 

  42. Fraenkel L, Fried T. If you want patients with knee osteoarthritis (OA) to exercise: tell them about NSAIDS. Patient. 2008;1(1):21–6.

    PubMed Central  PubMed  Google Scholar 

  43. Pieterse AH, et al. Benefit from preoperative radiotherapy in rectal cancer treatment: disease-free patients’ and oncologists’ preferences. Br J Cancer. 2007;97(6):717–24.

    CAS  PubMed Central  PubMed  Google Scholar 

  44. Stiggelbout AM, et al. Individual quality of life: adaptive conjoint analysis as an alternative for direct weighting? Qual Life Res. 2008;17(4):641–9.

    CAS  PubMed Central  PubMed  Google Scholar 

  45. Boonen LH, et al. Which preferred providers are really preferred? Effectiveness of insurers’ channeling incentives on pharmacy choice. Int J Health Care Finance Econ. 2009;9(4):347–66.

    PubMed  Google Scholar 

  46. Damman OC, et al. Consumers’ interpretation and use of comparative information on the quality of health care: the effect of presentation approaches. Health Expect. 2012;15(2):197–211.

    PubMed  Google Scholar 

  47. Davison SN, Kromm SK, Currie GR. Patient and health professional preferences for organ allocation and procurement, end-of-life care and organization of care for patients with chronic kidney disease using a discrete choice experiment. Nephrol Dial Transplant. 2010;25(7):2334–41.

    PubMed  Google Scholar 

  48. Eisingerich AB, et al. Attitudes and acceptance of oral and parenteral HIV preexposure prophylaxis among potential user groups: a multinational study. PLoS ONE. 2012;7(1):e28238.

    CAS  PubMed Central  PubMed  Google Scholar 

  49. Gerard K, et al. Valuing the extended role of prescribing pharmacist in general practice: results from a discrete choice experiment. Value Health. 2012;15(5):699–707.

    PubMed  Google Scholar 

  50. Gidengil CT, et al. Parental and societal values for the risks and benefits of childhood combination vaccines. Vaccine. 2012;30(23):3445–52.

    PubMed  Google Scholar 

  51. Goodall S, et al. Preferences for support services among adolescents and young adults with cancer or a blood disorder: a discrete choice experiment. Health Policy. 2012;107(2–3):304–11.

    PubMed  Google Scholar 

  52. Hancock-Howard RL, et al. Public preferences for counseling regarding antidepressant use during pregnancy: a discrete choice experiment. Birth Defects Res A Clin Mol Teratol. 2012;94(7):532–9.

    CAS  PubMed  Google Scholar 

  53. Hill M, et al. Women’s and health professionals’ preferences for prenatal tests for Down syndrome: a discrete choice experiment to contrast noninvasive prenatal diagnosis with current invasive tests. Genet Med. 2012;14(11):905–13.

    PubMed  Google Scholar 

  54. Kimman ML, et al. Follow-up after treatment for breast cancer: one strategy fits all? An investigation of patient preferences using a discrete choice experiment. Acta Oncol. 2010;49(3):328–37.

    PubMed  Google Scholar 

  55. Kruk ME, et al. Women’s preferences for obstetric care in rural Ethiopia: a population-based discrete choice experiment in a region with low rates of facility delivery. J Epidemiol Community Health. 2010;64(11):984–8.

    CAS  PubMed  Google Scholar 

  56. Landfeldt E, et al. Patient preferences for characteristics differentiating ovarian stimulation treatments. Hum Reprod. 2012;27(3):760–9.

    PubMed  Google Scholar 

  57. Mentzakis E, Ryan M, McNamee P. Using discrete choice experiments to value informal care tasks: exploring preference heterogeneity. Health Econ. 2011;20(8):930–44.

    PubMed  Google Scholar 

  58. Miners A, et al. Assessing user preferences for sexually transmitted infection testing services: a discrete choice experiment. Sex Transm Infect. 2012;88(7):510–6.

    PubMed Central  PubMed  Google Scholar 

  59. Mohamed AF, et al. Patient and parent preferences for immunoglobulin treatments: a conjoint analysis. J Med Econ. 2012;15(6):1183–91.

    PubMed  Google Scholar 

  60. Naik-Panvelkar P, et al. Patients’ value of asthma services in Australian pharmacies: the way ahead for asthma care. J Asthma. 2012;49(3):310–6.

    PubMed  Google Scholar 

  61. Naik-Panvelkar P, et al. Patient preferences for community pharmacy asthma services: a discrete choice experiment. Pharmacoeconomics. 2012;30(10):961–76.

    PubMed  Google Scholar 

  62. Pedersen LB, et al. Do general practitioners know patients’ preferences? An empirical study on the agency relationship at an aggregate level using a discrete choice experiment. Value Health. 2012;15(3):514–23.

    PubMed  Google Scholar 

  63. Poulos C, et al. Consumer preferences for household water treatment products in Andhra Pradesh, India. Soc Sci Med. 2012;75(4):738–46.

    PubMed  Google Scholar 

  64. van der Pol M, et al. Eliciting individual preferences for health care: a case study of perinatal care. Health Expect. 2010;13(1):4–12.

    PubMed  Google Scholar 

  65. Waltzman JT, Scholz T, Evans GR. What patients look for when choosing a plastic surgeon: an assessment of patient preference by conjoint analysis. Ann Plast Surg. 2011;66(6):643–7.

    CAS  PubMed  Google Scholar 

  66. Yeo ST, et al. Preferences of people with diabetes for diabetic retinopathy screening: a discrete choice experiment. Diabet Med. 2012;29(7):869–77.

    CAS  PubMed  Google Scholar 

  67. Yi D, et al. Using discrete choice experiments to inform randomised controlled trials: an application to chronic low back pain management in primary care. Eur J Pain. 2011;15(5):531e1–10.

    Google Scholar 

  68. Bederman S, Mahomed N. In the eye of the beholder: preferences of patients, family physicians, and surgeons for lumbar spinal surgery. Spine. 2009;35(1):108–15.

    Google Scholar 

  69. Bridges JF, et al. Patients’ preferences for treatment outcomes for advanced non-small cell lung cancer: a conjoint analysis. Lung Cancer. 2012;77(1):224–31.

    PubMed  Google Scholar 

  70. Chancellor J, et al. Stated preferences of physicians and chronic pain sufferers in the use of classic strong opioids. Value Health. 2012;15(1):106–17.

    PubMed  Google Scholar 

  71. Clark M, et al. Prioritizing patients for renal transplantation? Analysis of patient preferences for kidney allocation according to ethnicity and gender. J Divers Health Soc Care. 2009;6:181–91.

    Google Scholar 

  72. Clark MD, et al. Who should be prioritized for renal transplantation? Analysis of key stakeholder preferences using discrete choice experiments. BMC Nephrol. 2012;13:152.

    PubMed Central  PubMed  Google Scholar 

  73. Hauber AB, et al. Treatment preferences and medication adherence of people with type 2 diabetes using oral glucose-lowering agents. Diabet Med. 2009;26(4):416–24.

    CAS  PubMed  Google Scholar 

  74. Hauber AB, et al. Estimating importance weights for the IWQOL-Lite using conjoint analysis. Qual Life Res. 2010;19(5):701–9.

    PubMed  Google Scholar 

  75. Hauber AB, et al. Patient preferences for reducing toxicities of treatments for gastrointestinal stromal tumor (GIST). Patient Prefer Adherence. 2011;5:307–14.

    PubMed Central  PubMed  Google Scholar 

  76. Johnson FR, Hauber AB, Ozdemir S. Using conjoint analysis to estimate healthy-year equivalents for acute conditions: an application to vasomotor symptoms. Value Health. 2009;12(1):146–52.

    PubMed  Google Scholar 

  77. Potoglou D, et al. Best–worst scaling vs. discrete choice experiments: an empirical comparison using social care data. Soc Sci Med. 2011;72(10):1717–27.

    PubMed  Google Scholar 

  78. Ratcliffe J, et al. Using DCE and ranking data to estimate cardinal values for health states for deriving a preference-based single index from the sexual quality of life questionnaire. Health Econ. 2009;18(11):1261–76.

    PubMed  Google Scholar 

  79. van Til JA, Stiggelbout AM, Ijzerman MJ. The effect of information on preferences stated in a choice-based conjoint analysis. Patient Educ Couns. 2009;74(2):264–71.

    PubMed  Google Scholar 

  80. Wittink MN, et al. Towards patient-centered care for depression: conjoint methods to tailor treatment based on preferences. Patient. 2010;3(3):145–57.

    PubMed Central  PubMed  Google Scholar 

  81. Ahmed A, Fincham JE. Patients’ view of retail clinics as a source of primary care: boon for nurse practitioners? J Am Acad Nurse Pract. 2011;23(4):193–9.

    PubMed  Google Scholar 

  82. Albada A, Triemstra M. Patients’ priorities for ambulatory hospital care centres. A survey and discrete choice experiment among elderly and chronically ill patients of a Dutch hospital. Health Expect. 2009;12(1):92–105.

    PubMed  Google Scholar 

  83. Bansback N, et al. The effect of direct-to-consumer genetic tests on anticipated affect and health-seeking behaviors: a pilot survey. Genet Test Mol Biomarkers. 2012;16(10):1165–71.

    PubMed  Google Scholar 

  84. Bijlenga D, Bonsel GJ, Birnie E. Eliciting willingness to pay in obstetrics: comparing a direct and an indirect valuation method for complex health outcomes. Health Econ. 2011;20(11):1392–406.

    PubMed  Google Scholar 

  85. Bogelund M, et al. Patient preferences for diabetes management among people with type 2 diabetes in Denmark—a discrete choice experiment. Curr Med Res Opin. 2011;27(11):2175–83.

    PubMed  Google Scholar 

  86. Bridges JF, et al. Can patients diagnosed with schizophrenia complete choice-based conjoint analysis tasks? Patient. 2011;4(4):267–75.

    PubMed  Google Scholar 

  87. Bridges JF, et al. Consumer preferences for hearing aid attributes: a comparison of rating and conjoint analysis methods. Trends Amplif. 2012;16(1):40–8.

    PubMed  Google Scholar 

  88. Bridges JF, et al. Designing family-centered male circumcision services: a conjoint analysis approach. Patient. 2012;5(2):101–11.

    PubMed  Google Scholar 

  89. Brown DS, et al. Estimating older adults’ preferences for walking programs via conjoint analysis. Am J Prev Med. 2009;36(3):201–7 e4.

    PubMed  Google Scholar 

  90. Brown TM, et al. The perspective of patients with haemophilia with inhibitors and their care givers: preferences for treatment characteristics. Haemophilia. 2011;17(3):476–82.

    CAS  PubMed  Google Scholar 

  91. Bunge E, et al. Patients’ preferences for scoliosis brace treatment. Spine. 2009;35(1):57–63.

  92. Burnett HF, et al. Parents’ preferences for drug treatments in juvenile idiopathic arthritis: a discrete choice experiment. Arthritis Care Res (Hoboken). 2012;64(9):1382–91.

    Google Scholar 

  93. Chan YM, et al. Chinese women’s preferences for prenatal diagnostic procedure and their willingness to trade between procedures. Prenat Diagn. 2009;29(13):1270–6.

    PubMed  Google Scholar 

  94. Cheng J, et al. An empirical comparison of methods for analyzing correlated data from a discrete choice survey to elicit patient preference for colorectal cancer screening. BMC Med Res Methodol. 2012;12:15.

    PubMed Central  PubMed  Google Scholar 

  95. Damen TH, et al. Patients’ preferences for breast reconstruction: a discrete choice experiment. J Plast Reconstr Aesthet Surg. 2011;64(1):75–83.

    PubMed  Google Scholar 

  96. Darba J, et al. Patient preferences for osteoporosis in Spain: a discrete choice experiment. Osteoporos Int. 2011;22(6):1947–54.

    CAS  PubMed  Google Scholar 

  97. de Bekker-Grob EW, et al. Preferences of GPs and patients for preventive osteoporosis drug treatment: a discrete-choice experiment. Pharmacoeconomics. 2009;27(3):211–9.

    PubMed  Google Scholar 

  98. de Bekker-Grob EW, et al. Girls’ preferences for HPV vaccination: a discrete choice experiment. Vaccine. 2010;28(41):6692–7.

    PubMed  Google Scholar 

  99. Deverill M, et al. Antenatal care for first time mothers: a discrete choice experiment of women’s views on alternative packages of care. Eur J Obstet Gynecol Reprod Biol. 2010;151(1):33–7.

    CAS  PubMed  Google Scholar 

  100. Eberth B, et al. Does one size fit all? Investigating heterogeneity in men’s preferences for benign prostatic hyperplasia treatment using mixed logit analysis. Med Decis Making. 2009;29(6):707–15.

    PubMed  Google Scholar 

  101. Essers BA, et al. Assessing the public’s preference for surgical treatment of primary basal cell carcinoma: a discrete-choice experiment in the south of the Netherlands. Dermatol Surg. 2010;36(12):1950–5.

    CAS  PubMed  Google Scholar 

  102. Essers BA, et al. Does the inclusion of a cost attribute result in different preferences for the surgical treatment of primary basal cell carcinoma? A comparison of two discrete-choice experiments. Pharmacoeconomics. 2010;28(6):507–20.

    PubMed  Google Scholar 

  103. Faggioli G, et al. Preferences of patients, their family caregivers and vascular surgeons in the choice of abdominal aortic aneurysms treatment options: the PREFER study. Eur J Vasc Endovasc Surg. 2011;42(1):26–34.

    CAS  PubMed  Google Scholar 

  104. Glenngard AH, et al. Patient preferences and willingness-to-pay for ADHD treatment with stimulants using discrete choice experiment (DCE) in Sweden, Denmark and Norway. Nord J Psychiatry. 2013;67(5):351–9.

    PubMed  Google Scholar 

  105. Goto R, et al. A cohort study to examine whether time and risk preference is related to smoking cessation success. Addiction. 2009;104(6):1018–24.

    PubMed  Google Scholar 

  106. Goto R, Takahashi Y, Ida T. Changes in smokers’ attitudes toward intended cessation attempts in Japan. Value Health. 2011;14(5):785–91.

    PubMed  Google Scholar 

  107. Green C, Gerard K. Exploring the social value of health-care interventions: a stated preference discrete choice experiment. Health Econ. 2009;18(8):951–76.

    PubMed  Google Scholar 

  108. Guimaraes C, et al. A valuation of patients’ willingness-to-pay for insulin delivery in diabetes. Int J Technol Assess Health Care. 2009;25(3):359–66.

    PubMed  Google Scholar 

  109. Hodgkins P, et al. Patient preferences for first-line oral treatment for mild-to-moderate ulcerative colitis: a discrete-choice experiment. Patient. 2012;5(1):33–44.

    PubMed  Google Scholar 

  110. Hol L, et al. Preferences for colorectal cancer screening strategies: a discrete choice experiment. Br J Cancer. 2010;102(6):972–80.

    CAS  PubMed Central  PubMed  Google Scholar 

  111. Hong SH, et al. Conjoint analysis of patient preferences on Medicare medication therapy management. J Am Pharm Assoc (2003). 2011;51(3):378–87.

    Google Scholar 

  112. Howard K, Salkeld G. Does attribute framing in discrete choice experiments influence willingness to pay? Results from a discrete choice experiment in screening for colorectal cancer. Value Health. 2009;12(2):354–63.

    PubMed  Google Scholar 

  113. Howard K, et al. Preferences for CT colonography and colonoscopy as diagnostic tests for colorectal cancer: a discrete choice experiment. Value Health. 2011;14(8):1146–52.

    PubMed Central  PubMed  Google Scholar 

  114. Ijzerman MJ, van Til JA, Bridges JF. A comparison of analytic hierarchy process and conjoint analysis methods in assessing treatment alternatives for stroke rehabilitation. Patient. 2012;5(1):45–56.

    PubMed  Google Scholar 

  115. Johnson FR, Ozdemir S, Phillips KA. Effects of simplifying choice tasks on estimates of taste heterogeneity in stated-choice surveys. Soc Sci Med. 2010;70(2):183–90.

    PubMed Central  PubMed  Google Scholar 

  116. Kauf TL, et al. Patients’ willingness to accept the risks and benefits of new treatments for chronic hepatitis C virus infection. Patient. 2012;5(4):265–78.

    PubMed Central  PubMed  Google Scholar 

  117. Kinter ET, et al. A comparison of two experimental design approaches in applying conjoint analysis in patient-centered outcomes research: a randomized trial. Patient. 2012;5(4):279–94.

    PubMed  Google Scholar 

  118. Kiiskinen U, Suominen-Taipale AL, Cairns J. Think twice before you book? Modelling the choice of public vs private dentist in a choice experiment. Health Econ. 2010;19(6):670–82.

    PubMed  Google Scholar 

  119. Koopmanschap MA, Stolk EA, Koolman X. Dear policy maker: have you made up your mind? A discrete choice experiment among policy makers and other health professionals. Int J Technol Assess Health Care. 2010;26(2):198–204.

    PubMed  Google Scholar 

  120. Kruijshaar ME, et al. A labelled discrete choice experiment adds realism to the choices presented: preferences for surveillance tests for Barrett esophagus. BMC Med Res Methodol. 2009;9:31.

    PubMed Central  PubMed  Google Scholar 

  121. Laba TL, Brien JA, Jan S. Understanding rational non-adherence to medications. A discrete choice experiment in a community sample in Australia. BMC Fam Pract. 2012;13:61.

    PubMed Central  PubMed  Google Scholar 

  122. Lagarde M, Smith Paintain L. Evaluating health workers’ potential resistance to new interventions: a role for discrete choice experiments. PLoS ONE. 2011;6(8):e23588.

    CAS  PubMed Central  PubMed  Google Scholar 

  123. Laver K, et al. Early rehabilitation management after stroke: what do stroke patients prefer? J Rehabil Med. 2011;43(4):354–8.

    PubMed  Google Scholar 

  124. de Bekker-Grob EW, Rose JM, Bliemer MC. A closer look at decision and analyst error by including nonlinearities in discrete choice models: implications on willingness-to-pay estimates derived from discrete choice data in healthcare. Pharmacoeconomics. 2013;31(12):1169–83.

  125. Lloyd A, et al. Methylphenidate delivery mechanisms for the treatment of children with attention deficit hyperactivity disorder: heterogeneity in parent preferences. Int J Technol Assess Health Care. 2011;27(3):215–23.

    PubMed  Google Scholar 

  126. Lloyd A, et al. Willingness to pay for improvements in chronic long-acting insulin therapy in individuals with type 1 or type 2 diabetes mellitus. Clin Ther. 2011;33(9):1258–67.

    PubMed  Google Scholar 

  127. Manjunath R, Yang JC, Ettinger AB. Patients’ preferences for treatment outcomes of add-on antiepileptic drugs: a conjoint analysis. Epilepsy Behav. 2012;24(4):474–9.

    PubMed  Google Scholar 

  128. Marti J. Assessing preferences for improved smoking cessation medications: a discrete choice experiment. Eur J Health Econ. 2012;13(5):533–48.

    PubMed  Google Scholar 

  129. Mentzakis E, Stefanowska P, Hurley J. A discrete choice experiment investigating preferences for funding drugs used to treat orphan diseases: an exploratory study. Health Econ Policy Law. 2011;6(3):405–33.

    PubMed  Google Scholar 

  130. Mohamed AF, Epstein JD, Li-McLeod JM. Patient and parent preferences for haemophilia A treatments. Haemophilia. 2011;17(2):209–14.

    CAS  PubMed  Google Scholar 

  131. Morton RL, et al. Factors influencing patient choice of dialysis versus conservative care to treat end-stage kidney disease. CMAJ. 2012;184(5):E277–83.

    PubMed Central  PubMed  Google Scholar 

  132. Morton RL, et al. Dialysis modality preference of patients with CKD and family caregivers: a discrete-choice study. Am J Kidney Dis. 2012;60(1):102–11.

    PubMed  Google Scholar 

  133. Muhlbacher AC, Nubling M. Analysis of physicians’ perspectives versus patients’ preferences: direct assessment and discrete choice experiments in the therapy of multiple myeloma. Eur J Health Econ. 2011;12(3):193–203.

    PubMed  Google Scholar 

  134. Muhlbacher AC, et al. Preferences for treatment of attention-deficit/hyperactivity disorder (ADHD): a discrete choice experiment. BMC Health Serv Res. 2009;9:149.

    PubMed Central  PubMed  Google Scholar 

  135. Musters AM, et al. Women’s perspectives regarding subcutaneous injections, costs and live birth rates in IVF. Hum Reprod. 2011;26(9):2425–31.

    CAS  PubMed  Google Scholar 

  136. Nayaradou M, et al. Eliciting population preferences for mass colorectal cancer screening organization. Med Decis Making. 2010;30(2):224–33.

    PubMed  Google Scholar 

  137. Nieboer AP, Koolman X, Stolk EA. Preferences for long-term care services: willingness to pay estimates derived from a discrete choice experiment. Soc Sci Med. 2010;70(9):1317–25.

    PubMed  Google Scholar 

  138. Ozdemir S, Johnson FR, Hauber AB. Hypothetical bias, cheap talk, and stated willingness to pay for health care. J Health Econ. 2009;28(4):894–901.

    PubMed  Google Scholar 

  139. Park MH, et al. A comparison of preferences of targeted therapy for metastatic renal cell carcinoma between the patient group and health care professional group in South Korea. Value Health. 2012;15(6):933–9.

    PubMed  Google Scholar 

  140. Pavlova M, et al. The choice of obstetric care by low-risk pregnant women in the Netherlands: implications for policy and management. Health Policy. 2009;93(1):27–34.

    PubMed  Google Scholar 

  141. Pereira CC, et al. Determinants of influenza vaccine purchasing decision in the US: a conjoint analysis. Vaccine. 2011;29(7):1443–7.

    PubMed  Google Scholar 

  142. Pignone MP, et al. Conjoint analysis versus rating and ranking for values elicitation and clarification in colorectal cancer screening. J Gen Intern Med. 2012;27(1):45–50.

    PubMed Central  PubMed  Google Scholar 

  143. Regier DA, et al. Discrete choice experiment to evaluate factors that influence preferences for antibiotic prophylaxis in pediatric oncology. PLoS ONE. 2012;7(10):e47470.

    CAS  PubMed Central  PubMed  Google Scholar 

  144. Ryan M, Watson V. Comparing welfare estimates from payment card contingent valuation and discrete choice experiments. Health Econ. 2009;18(4):389–401.

    PubMed  Google Scholar 

  145. Scalone L, et al. Patients’, physicians’, and pharmacists’ preferences towards coagulation factor concentrates to treat haemophilia with inhibitors: results from the COHIBA Study. Haemophilia. 2009;15(2):473–86.

    CAS  PubMed  Google Scholar 

  146. Scalone L, et al. Evaluation of patients’ preferences for genital herpes treatment. Sex Transm Dis. 2011;38(9):802–7.

    PubMed  Google Scholar 

  147. Schaarschmidt ML, et al. Patient preferences for psoriasis treatments: process characteristics can outweigh outcome attributes. Arch Dermatol. 2011;147(11):1285–94.

    PubMed  Google Scholar 

  148. Schwappach DL, et al. Is less more? Patients’ preferences for drug information leaflets. Pharmacoepidemiol Drug Saf. 2011;20(9):987–95.

    PubMed  Google Scholar 

  149. Scotland GS, et al. Women’s preferences for aspects of labor management: results from a discrete choice experiment. Birth. 2011;38(1):36–46.

    PubMed  Google Scholar 

  150. Skjoldborg US, Lauridsen J, Junker P. Reliability of the discrete choice experiment at the input and output level in patients with rheumatoid arthritis. Value Health. 2009;12(1):153–8.

    PubMed  Google Scholar 

  151. Sung L, et al. Discrete choice experiment produced estimates of acceptable risks of therapeutic options in cancer patients with febrile neutropenia. J Clin Epidemiol. 2012;65(6):627–34.

    PubMed  Google Scholar 

  152. Sweeting KR, et al. Patient preferences for treatment of Achilles tendon pain: results from a discrete-choice experiment. Patient. 2011;4(1):45–54.

    PubMed  Google Scholar 

  153. Swinburn P, et al. Preferences for antimuscarinic therapy for overactive bladder. BJU Int. 2011;108(6):868–73.

    PubMed  Google Scholar 

  154. Thrumurthy SG, et al. Discrete-choice preference comparison between patients and doctors for the surgical management of oesophagogastric cancer. Br J Surg. 2011;98(8):1124-31 (discussion 1132).

  155. Tinelli M, Ryan M, Bond C. Patients’ preferences for an increased pharmacist role in the management of drug therapy. Int J Pharm Pract. 2009;17(5):275–82.

    PubMed  Google Scholar 

  156. Tinelli M, et al. What determines patient preferences for treating low risk basal cell carcinoma when comparing surgery vs imiquimod? A discrete choice experiment survey from the SINS trial. BMC Dermatol. 2012;12:19.

    PubMed Central  PubMed  Google Scholar 

  157. van Dam L, et al. What determines individuals’ preferences for colorectal cancer screening programmes? A discrete choice experiment. Eur J Cancer. 2010;46(1):150–9.

    PubMed  Google Scholar 

  158. van der Pol M, McKenzie L. Costs and benefits of tele-endoscopy clinics in a remote location. J Telemed Telecare. 2010;16(2):89–94.

    PubMed  Google Scholar 

  159. van Empel IW, et al. Physicians underestimate the importance of patient-centredness to patients: a discrete choice experiment in fertility care. Hum Reprod. 2011;26(3):584–93.

    PubMed  Google Scholar 

  160. Van Houtven G, et al. Eliciting benefit-risk preferences and probability-weighted utility using choice-format conjoint analysis. Med Decis Making. 2011;31(3):469–80.

    PubMed  Google Scholar 

  161. Witt J, Scott A, Osborne RH. Designing choice experiments with many attributes. An application to setting priorities for orthopaedic waiting lists. Health Econ. 2009;18(6):681–96.

    PubMed  Google Scholar 

  162. Wong MK, et al. Patients rank toxicity against progression free survival in second-line treatment of advanced renal cell carcinoma. J Med Econ. 2012;15(6):1139–48.

    PubMed  Google Scholar 

  163. Bansback N, et al. Using a discrete choice experiment to estimate health state utility values. J Health Econ. 2012;31(1):306–18.

    PubMed  Google Scholar 

  164. Stolk EA, et al. Discrete choice modeling for the quantification of health states: the case of the EQ-5D. Value Health. 2010;13(8):1005–13.

    PubMed  Google Scholar 

  165. Lancsar E, et al. Deriving distributional weights for QALYs through discrete choice experiments. J Health Econ. 2011;30(2):466–78.

    PubMed  Google Scholar 

  166. van der Wulp I, et al. Societal preferences for standard health insurance coverage in the Netherlands: a cross-sectional study. BMJ Open. 2012;2(2):e001021.

    PubMed Central  PubMed  Google Scholar 

  167. Blaauw D, et al. Policy interventions that attract nurses to rural areas: a multicountry discrete choice experiment. Bull World Health Organ. 2010;88(5):350–6.

    CAS  PubMed Central  PubMed  Google Scholar 

  168. Grindrod KA, et al. Pharmacists’ preferences for providing patient-centered services: a discrete choice experiment to guide health policy. Ann Pharmacother. 2010;44(10):1554–64.

    PubMed  Google Scholar 

  169. Gunther OH, et al. The role of monetary and nonmonetary incentives on the choice of practice establishment: a stated preference study of young physicians in Germany. Health Serv Res. 2010;45(1):212–29.

    PubMed Central  PubMed  Google Scholar 

  170. Huicho L, et al. Job preferences of nurses and midwives for taking up a rural job in Peru: a discrete choice experiment. PLoS ONE. 2012;7(12):e50315.

    CAS  PubMed Central  PubMed  Google Scholar 

  171. Kolstad JR. How to make rural jobs more attractive to health workers. Findings from a discrete choice experiment in Tanzania. Health Econ. 2011;20(2):196–211.

    PubMed  Google Scholar 

  172. Miranda JJ, et al. Stated preferences of doctors for choosing a job in rural areas of Peru: a discrete choice experiment. PLoS ONE. 2012;7(12):e50567.

    CAS  PubMed Central  PubMed  Google Scholar 

  173. Rockers PC, et al. Preferences for working in rural clinics among trainee health professionals in Uganda: a discrete choice experiment. BMC Health Serv Res. 2012;12:212.

    PubMed Central  PubMed  Google Scholar 

  174. Sivey P, et al. Junior doctors’ preferences for specialty choice. J Health Econ. 2012;31(6):813–23.

    PubMed  Google Scholar 

  175. Carlsen B, et al. When you can’t have the cake and eat it too: a study of medical doctors’ priorities in complex choice situations. Soc Sci Med. 2012;75(11):1964–73.

    PubMed  Google Scholar 

  176. Defechereux T, et al. Health care priority setting in Norway a multicriteria decision analysis. BMC Health Serv Res. 2012;12:39.

    PubMed Central  PubMed  Google Scholar 

  177. Diederich A, Swait J, Wirsik N. Citizen participation in patient prioritization policy decisions: an empirical and experimental study on patients’ characteristics. PLoS ONE. 2012;7(5):e36824.

    CAS  PubMed Central  PubMed  Google Scholar 

  178. Kjaer T, et al. Public preferences for establishing nephrology facilities in Greenland: estimating willingness-to-pay using a discrete choice experiment. Eur J Health Econ. 2013;14(5):739–48.

    PubMed  Google Scholar 

  179. Lim MK, et al. Eliciting public preference for health-care resource allocation in South Korea. Value Health. 2012;15(1 Suppl):S91–4.

    PubMed  Google Scholar 

  180. Marsh K, et al. Prioritizing investments in public health: a multi-criteria decision analysis. J Public Health (Oxf). 2013;35(3):460–6.

    CAS  Google Scholar 

  181. Mirelman A, et al. Decision-making criteria among national policymakers in five countries: a discrete choice experiment eliciting relative preferences for equity and efficiency. Value Health. 2012;15(3):534–9.

    PubMed  Google Scholar 

  182. Ng V, Sargeant JM. A quantitative and novel approach to the prioritization of zoonotic diseases in North America: a public perspective. PLoS One. 2012;7(11):e48519.

    CAS  PubMed Central  PubMed  Google Scholar 

  183. Philips H, et al. Predicting the place of out-of-hours care—a market simulation based on discrete choice analysis. Health Policy. 2012;106(3):284–90.

    PubMed  Google Scholar 

  184. Promberger M, Dolan P, Marteau TM. “Pay them if it works”: discrete choice experiments on the acceptability of financial incentives to change health related behaviour. Soc Sci Med. 2012;75(12):2509–14.

    PubMed Central  PubMed  Google Scholar 

  185. Rennie L, Porteous T, Ryan M. Preferences for managing symptoms of differing severity: a discrete choice experiment. Value Health. 2012;15(8):1069–76.

    PubMed  Google Scholar 

  186. Scuffham PA, et al. Health system choice: a pilot discrete-choice experiment eliciting the preferences of British and Australian citizens. Appl Health Econ Health Policy. 2010;8(2):89–97.

    PubMed  Google Scholar 

  187. Watson V, et al. Involving the public in priority setting: a case study using discrete choice experiments. J Public Health (Oxf). 2012;34(2):253–60.

    Google Scholar 

  188. Watson V, et al. Managing poorly performing clinicians: health care providers’ willingness to pay for independent help. Health Policy. 2012;104(3):260–71.

    PubMed  Google Scholar 

  189. Youngkong S, et al. Criteria for priority setting of HIV/AIDS interventions in Thailand: a discrete choice experiment. BMC Health Serv Res. 2010;10:197.

    PubMed Central  PubMed  Google Scholar 

  190. Arden NK, et al. How do physicians weigh benefits and risks associated with treatments in patients with osteoarthritis in the United Kingdom? J Rheumatol. 2012;39(5):1056–63.

    PubMed  Google Scholar 

  191. Benjamin L, et al. Physicians’ preferences for prescribing oral and intravenous anticancer drugs: a discrete choice experiment. Eur J Cancer. 2012;48(6):912–20.

    PubMed  Google Scholar 

  192. Bhatt M, et al. Current practice and tolerance for risk in performing procedural sedation and analgesia on children who have not met fasting guidelines: a Canadian survey using a stated preference discrete choice experiment. Acad Emerg Med. 2010;17(11):1207–15.

    PubMed  Google Scholar 

  193. Jackman J, et al. Minding the gap: an approach to determine critical drivers in the development of point of care diagnostics. Point Care. 2012;11(2):130–9.

    PubMed Central  PubMed  Google Scholar 

  194. Mohamed AF, et al. Physicians’ stated trade-off preferences for chronic hepatitis B treatment outcomes in Germany, France, Spain, Turkey, and Italy. Eur J Gastroenterol Hepatol. 2012;24(4):419–26.

    PubMed  Google Scholar 

  195. Nathan H, et al. Treating patients with colon cancer liver metastasis: a nationwide analysis of therapeutic decision making. Ann Surg Oncol. 2012;19(12):3668–76.

    PubMed Central  PubMed  Google Scholar 

  196. Torbica A, Fattore G. Understanding the impact of economic evidence on clinical decision making: a discrete choice experiment in cardiology. Soc Sci Med. 2010;70(10):1536–43.

    PubMed  Google Scholar 

  197. Tsung-Tai C, Heng-Chaing C, Lao-Nga M. Using discrete choice experiments to elicit doctors’ preferences for report card design of diabetes care in Taiwan—a pilot study. J Eval Clin Pract. 2010;16:14–20.

    Google Scholar 

  198. van Helvoort-Postulart D, et al. Discrete choice experiments for complex health-care decisions: does hierarchical information integration offer a solution? Health Econ. 2009;18(8):903–20.

    PubMed  Google Scholar 

  199. van Helvoort-Postulart D, van der Weijden T. Investigating the complementary value of discrete choice experiments for the evaluation of barriers and facilitators in implementation research: a questionnaire survey. Implement Sci. 2009;4.

  200. Wyatt JC, Batley RP, Keen J. GP preferences for information systems: conjoint analysis of speed, reliability, access and users. J Eval Clin Pract. 2010;16(5):911–5.

    PubMed  Google Scholar 

  201. Pedersen LB, et al. General practitioners’ preferences for the organisation of primary care: a discrete choice experiment. Health Policy. 2012;106(3):246–56.

    PubMed  Google Scholar 

  202. Al Hamarneh YN, et al. Public perceptions of coronary events risk factors: a discrete choice experiment. BMJ Open. 2012;2(5).

  203. Bech M, Kjaer T, Lauridsen J. Does the number of choice sets matter? Results from a web survey applying a discrete choice experiment. Health Econ. 2011;20(3):273–86.

    PubMed  Google Scholar 

  204. Cunningham CE, et al. Preferences for evidence-based practice dissemination in addiction agencies serving women: a discrete-choice conjoint experiment. Addiction. 2012;107(8):1512–24.

    PubMed  Google Scholar 

  205. Fegert JM, et al. Assessment of parents’ preferences for the treatment of school-age children with ADHD: a discrete choice experiment. Expert Rev Pharmacoecon Outcomes Res. 2011;11(3):245–52.

    PubMed  Google Scholar 

  206. Oteng B, Marra F, Another A. Evaluating societal preferences for human papillomavirus vaccine and cervical smear test screening programme. Sex Transm Infect. 2011;87(1):52–7.

    PubMed  Google Scholar 

  207. Regier DA, et al. Valuing the benefit of diagnostic testing for genetic causes of idiopathic developmental disability: willingness to pay from families of affected children. Clin Genet. 2009;75(6):514–21.

    CAS  PubMed  Google Scholar 

  208. Robyn PJ, et al. Health worker preferences for community-based health insurance payment mechanisms: a discrete choice experiment. BMC Health Serv Res. 2012;12:159.

    PubMed Central  PubMed  Google Scholar 

  209. Scasny M, Alberini A. Valuation of mortality risk attributable to climate change: investigating the effect of survey administration modes on a VSL. Int J Environ Res Public Health. 2012;9(12):4760–81.

    PubMed Central  PubMed  Google Scholar 

  210. Schellings R, et al. The development of quality indicators in mental healthcare: a discrete choice experiment. BMC Psychiatry. 2012;12:103.

    PubMed Central  PubMed  Google Scholar 

  211. Tong BC, et al. Weighting composite endpoints in clinical trials: essential evidence for the heart team. Ann Thorac Surg. 2012;94(6):1908–13.

    PubMed Central  PubMed  Google Scholar 

  212. Tonin S, Alberini A, Turvani M. The value of reducing cancer risks at contaminated sites: are more knowledgeable people willing to pay more? Risk Anal. 2012;32(7):1157–82.

    PubMed  Google Scholar 

  213. Vroomen J, Zweifel P. Preferences for health insurance and health status: does it matter whether you are Dutch or German? Eur J Health Econ. 2011;12(1):87–95.

    Google Scholar 

  214. Whitty JA, Scuffham PA, Rundle-Thiele SR. Public and decision maker stated preferences for pharmaceutical subsidy decisions: a pilot study. Appl Health Econ Health Policy. 2011;9(2):73–9.

    PubMed  Google Scholar 

  215. Idkowiak J, et al. Premature adrenarche: novel lessons from early onset androgen excess. Eur J Endocrinol. 2011;165(2):189–207.

    CAS  PubMed  Google Scholar 

  216. Hole AR. Modelling heterogeneity in patients’ preferences for the attributes of a general practitioner appointment. J Health Econ. 2008;27(4):1078–94.

    PubMed  Google Scholar 

  217. Mark TL, Swait J. Using stated preference modeling to forecast the effect of medication attributes on prescriptions of alcoholism medications. Value Health. 2003;6(4):474–82.

    PubMed  Google Scholar 

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

    PubMed  Google Scholar 

  219. 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):535–46.

    CAS  PubMed  Google Scholar 

  220. Ryan M, 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):927–44.

    PubMed  Google Scholar 

  221. Burr JM, et al. Developing a preference-based Glaucoma Utility Index using a discrete choice experiment. Optom Vis Sci. 2007;84(8):797–808.

    PubMed  Google Scholar 

  222. Slothuus Skjoldborg U, Gyrd-Hansen D. Conjoint analysis. The cost variable: an Achilles’ heel? Health Econ. 2003;12(6):479–91.

    PubMed  Google Scholar 

  223. Gyrd-Hansen D, Skjoldborg US. The price proxy in discrete choice experiments: Issues of relevance for future research. In: Ryan M, Gerard K, Amaya-Amaya M, editors. Using discrete choice experiments to value health and health care; 2008. p. 175–193.

  224. Louviere JJ, Lancsar E. Choice experiments in health: the good, the bad, the ugly and toward a brighter future. Health Econ Policy Law. 2009;4(Pt 4):527–46.

    PubMed  Google Scholar 

  225. Mark TL, Swait J. Using stated preference and revealed preference modeling to evaluate prescribing decisions. Health Econ. 2004;13(6):563–73.

    PubMed  Google Scholar 

  226. Johnson FR, et al. How does cost matter in health-care discrete-choice experiments? Health Econ. 2011;20(3):323–30.

    PubMed Central  PubMed  Google Scholar 

  227. Ratcliffe J, et al. Patients’ preferences for characteristics associated with treatments for osteoarthritis. Rheumatology (Oxford). 2004;43(3):337–45.

    CAS  Google Scholar 

  228. Deal K. Segmenting patients and physicians using preferences from discrete choice experiments. Patient. 2014;7(1):5–21.

    PubMed  Google Scholar 

  229. Miguel FS, Ryan M, Amaya-Amaya M. ‘Irrational’ stated preferences: a quantitative and qualitative investigation. Health Econ. 2005;14(3):307–22.

    PubMed  Google Scholar 

Download references

Conflict of interest

Dr. Michael D. Clark: no conflict of interest. Mr. Clark wrote the drafts of the paper, and then took on board feedback from co-authors and peer reviewers in order to further refine it. He will act as overall guarantor for this work. He also evaluated many of the analyses relating to the new review period (2009–2012), and conducted some of the literature searches. Dr. Domino Determann: no conflict of interest. Determann MD reviewed a significant proportion of DCE papers relating to the period 2009–2012, and conducted many of the literature searches. She also provided feedback on early drafts of the paper and suggested some amendments.

Professor Stavros Petrou: no conflict of interest. Professor Petrou supervised this new DCE review from the outset, and commented on drafts of the paper, suggesting edits.

Dr. Domenico Moro: no conflict of interest. Dr. Moro reviewed some papers involving the use of mixed logit or latent class models. He was part of the review team from the onset, and commented on drafts of the paper when appropriate.

Dr. Esther de Bekker-Grob: no conflict of interest. As the first author of a high-profile published review of the DCE literature [3] which reviewed the DCE literature for the period 2001–2008, this co-author helped to ensure consistency with the earlier published review in terms of application of review criteria. She also commented on drafts of the paper, and made some valuable contributions to the points raised by the paper in Sects. 9 and 10.

Ethics committee approval

Not required.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Michael D. Clark.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 159 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Clark, M.D., Determann, D., Petrou, S. et al. Discrete Choice Experiments in Health Economics: A Review of the Literature. PharmacoEconomics 32, 883–902 (2014). https://doi.org/10.1007/s40273-014-0170-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40273-014-0170-x

Keywords

Navigation