The Patient: Patient-Centered Outcomes Research

, Volume 1, Issue 4, pp 273–282

Things are Looking up Since We Started Listening to Patients

Trends in the Application of Conjoint Analysis in Health 1982–2007
  • John F.P. Bridges
  • Elizabeth T. Kinter
  • Lillian Kidane
  • Rebekah R. Heinzen
  • Colleen McCormick
Conference Paper

Abstract

Clinical and healthcare decision makers have repeatedly endorsed patient-centered care as a goal of the health system. However, traditional methods of evaluation reinforce societal views, and research focusing on views of patients is often referred to as ‘soft science.’ Conjoint analysis presents a scientifically rigorous research tool that can be used to understand patient preferences and inform decision making. This paper documents applications of conjoint analysis in medicine and systematically reviews this literature in order to identify publication trends and the range of topics to which conjoint analysis has been applied. In addition, we document important methodological aspects such as sample size, experimental design, and method of analysis.

Publications were identified through a MEDLINE search using multiple search terms for identification. We classified each article into one of three categories: clinical applications (n = 122); methodological contributions (n = 56); and health system applications (n = 47). Articles that did not use or adequately discuss conjoint analysis methods (n = 164) were discarded. We identified a near exponential increase in the application of conjoint analyses over the last 10 years of the study period (1997–2007). Over this period, the proportion of applications on clinical topics increased from 40% of articles published in MEDLINE from 1998 to 2002, to 64% of articles published from 2003 to 2007 (p = 0.002).

The average sample size among articles focusing on health system applications (n = 556) was significantly higher than clinical applications (n = 277) [p = 0.001], although this 2-fold difference was primarily due to a number of outliers reporting sample sizes in the thousands. The vast majority of papers claimed to use orthogonal factorial designs, although over a quarter of papers did not report their design properties. In terms of types of analysis, logistic regression was favored among clinical applications (28%), while probit was most commonly used among health systems applications (38%). However, 25% of clinical applications and 33% of health systems articles failed to report what regression methods were used. We used the International Classification of Diseases — version 9 (ICD-9) coding system to categorize clinical applications, with approximately 26% of publications focusing on neoplasm. Program planning and evaluation applications accounted for 22% of the health system articles.

While interest in conjoint analysis in health is likely to continue, better guidelines for conducting and reporting conjoint analyses are needed.

Supplementary material

40271_2012_1040273_MOESM1_ESM.pdf (135 kb)
Supplementary material, approximately 138 KB.

References

  1. 1.
    Institute of Medicine. To err is human: building a safer health system. Washington, DC: National Academy Press, 2000Google Scholar
  2. 2.
    Agency for Healthcare Research and Quality. National healthcare quality report [report no.: 07-0013]. Rockville (MD): Agency for Healthcare Research and Quality, 2006Google Scholar
  3. 3.
    Krumholz H, Peterson E, Ayanian J, et al. Report of the National Heart, Lung and Blood Institute working group on outcomes research in cardiovascular disease. Circulation 2005; 111: 3158–66PubMedCrossRefGoogle Scholar
  4. 4.
    Bridges J. What can economics add to health technology assessment? Please not another cost-effectiveness analysis! Exp Rev Pharmacoeconomics Outcomes Res 2006; 6(1): 19–24CrossRefGoogle Scholar
  5. 5.
    Bridges J, Jones C. Lean systems approaches to health technology assessment: a patient-focused alternative to cost-effectiveness analysis. Pharmacoeconomics 2006; 24(S2): 101–9PubMedGoogle Scholar
  6. 6.
    US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER), Center for Devices and Radiological Health (CDRH). Guidance for industry: patient-reported outcome measures. Use in medical product development to support labeling claims: draft guidance. February 2006 [online]. Available from URL: http://www.fda.gov/Cder/guidance/5460dft.pdf [Accessed 2008 Jan 28]
  7. 7.
    NHS Executive. Patient and public involvement in the new NHS. Leeds: Department of Health, 1999Google Scholar
  8. 8.
    Vogt F, Schwappach DL, Bridges JF. Accounting for tastes: a German perspective on the inclusion of patient preferences in healthcare. Pharmacoeconomics 2006; 24(5): 419–23PubMedCrossRefGoogle Scholar
  9. 9.
    Bridges JF, Jones C. Patient-based health technology assessment: a vision of the future. Int J Technol Assess Health Care 2007 Winter; 23(1): 30–5PubMedCrossRefGoogle Scholar
  10. 10.
    Bridges JF. Stated preference methods in health care evaluation: an emerging methodological paradigm in health economics. Appl Health Econ Health Policy 2003; 2(4): 213–24PubMedGoogle Scholar
  11. 11.
    Green P, Rao V. Conjoint measurement for quantifying judgmental data. J Mark Res 1971; 8: 355–63CrossRefGoogle Scholar
  12. 12.
    Luce R, Tukey J. Simulataneous conjoint measurement: a new type of fundamental measurement. J Math Psychol 1964; 1: 1–27CrossRefGoogle Scholar
  13. 13.
    Gustafsson A, Herrmann A, Huber F. Conjoint analysis as an instrument of market research practice. In: Gustafsson A, Herrmann A, Huber F, editors. Conjoint measurement: methods and applications. Berlin: Springer, 2003: 3–30CrossRefGoogle Scholar
  14. 14.
    Green D. Estimating daily vehicle usage distributions and the implications for limited-range vehicles. Transp Res 1985; 19B(4): 347–58CrossRefGoogle Scholar
  15. 15.
    Segal R. Forecasting the market for electric vehicles in California using conjoint analysis. Energy J 1995; 16(6): 89–112Google Scholar
  16. 16.
    Cattin P, Wittink D. Commencai use of conjoint analysis: a survey. J Mark 1982; 46: 44–53CrossRefGoogle Scholar
  17. 17.
    Wittink D, Cattin P. Commercial use of conjoint analysis: an update. J Mark 1989; 53: 91–6CrossRefGoogle Scholar
  18. 18.
    Kroes EP, Sheldon RJ. Stated preference methods: an introduction. JTEP 1988; 22(1): 11–25Google Scholar
  19. 19.
    Fowkes T, Wardman M. The design of stated preference travel choice experiments, with special reference to inter-personal taste variations. JTEP 1988; 22(1): 27–44Google Scholar
  20. 20.
    Hensher DA, Barnard PO, Truong TP. The role of stated preference methods in studies of travel choice. JTEP 1988; 22(1): 45–58Google Scholar
  21. 21.
    Bates J. Econometric issues in stated preference analysis. JTEP 1988; 22(1): 59–69Google Scholar
  22. 22.
    Wardman M. A comparison of revealed preference and stated preference models of travel behaviour. JTEP 1988; 22(1): 71–91Google Scholar
  23. 23.
    Louviere JJ. Conjoint analysis modelling of stated preferences: a review of theory, methods, recent developments and external validity. JTEP 1988; 22(1): 93–119Google Scholar
  24. 24.
    Bradley M. Realism and adaptation in designing hypothetical travel choice concepts. JTEP 1988; 22(1): 121–37Google Scholar
  25. 25.
    Rae D. Visibility impairment at Mesa Verde National Park: an analysis of benefits and costs of controlling emissions in the Four Corners area. Boston (MA): Electric Power Research Institute, 1981Google Scholar
  26. 26.
    Rae D. Benefits of improving visibility at Great Smoky National Park [draft]. Boston (MA): Electric Power Research Institute, 1981Google Scholar
  27. 27.
    Pascoe G. Patient satisfaction in primary health care: a literature review and analysis. Eval Program Plann 1983; 6: 185–210PubMedCrossRefGoogle Scholar
  28. 28.
    Louviere J. Conjoint analysis modeling of stated preferences. J Transport Econ Pol 1988; 22(1): 93–119Google Scholar
  29. 29.
    Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future reflections. Health Econ Health Policy 2003; 2(1): 55–64Google Scholar
  30. 30.
    Ryan M, Bate A, Eastmond C, et al. Use of discrete choice experiments to elicit preferences. Qual Saf Health Care 2001 Sep; 10Suppl. 1: i55–60CrossRefGoogle Scholar
  31. 31.
    Lancaster K. A new approach to consumer theory. J Polit Econ 1966; 74: 132–57CrossRefGoogle Scholar
  32. 32.
    Bingham MF, Johnson FR, Miller D. Modeling choice behavior for new pharmaceutical products. Value Health 2001 Jan–Feb; 4(1): 32–44PubMedCrossRefGoogle Scholar
  33. 33.
    Ratcliffe J. The use of conjoint analysis to elicit willingness-to-pay values: proceed with caution? Int J Technol Assess Health Care 2000; 16(1): 270–5PubMedCrossRefGoogle Scholar
  34. 34.
    Adamowicz J, Williams M. Combining revealed preference and stated preference methods for valuing environmental amenities. J Environ Econ Manage 1994; 6(6): 271–92CrossRefGoogle Scholar
  35. 35.
    Hedayat A, Sloane J, Stufken J. Orthogonal arrays. New York: Springer-Verlag, 1999CrossRefGoogle Scholar
  36. 36.
    Louviere J, Hensher D, Swait J. Stated choice methods: analysis and application. Cambridge (UK): Cambridge University Press, 2000CrossRefGoogle Scholar
  37. 37.
    Rose J, Bliemer M. Designing stated choice experiments: the state of the art. 1 1th International Conference on Travel Behaviour Research; 2006 Aug 16–20; KyotoGoogle Scholar
  38. 38.
    Kuhfeld W, Tobias R, Garratt M. Efficient experimental design with marketing research applications. J Mark Res 1994; 11: 545–57CrossRefGoogle Scholar
  39. 39.
    Orme B. Sample size issues and conjoint analysis: getting started with conjoint analysis. Strategies for product design and pricing research. Madison (WI): Research Publishers LLC, 1998Google Scholar
  40. 40.
    Train K. Discrete choice methods with simulation. Cambridge (UK): Cambridge University Press, 2003CrossRefGoogle Scholar
  41. 41.
    Ryan M, Gerard K. Using discrete choice experiments in health economics: moving forward. In: Scott A, Mayndard A, Elliot R, editors. Advances in health economics. Hoboken (NJ): John Wiley and Sons, Ltd, 2003Google Scholar
  42. 42.
    Phillips KA, Maddala T, Johnson FR. Measuring preferences for health care interventions using conjoint analysis: an application to HIV testing. Health Serv Res 2002 Dec; 37(6): 1681–705PubMedCrossRefGoogle Scholar
  43. 43.
    Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ 2000 Jun 3; 320(7248): 1530–3PubMedCrossRefGoogle Scholar
  44. 44.
    Ryan M, Scott DA, Reeves C, et al. Eliciting public preferences for healthcare: a systematic review of techniques. Health Technol Assess 2001; 5(5): 1–186PubMedGoogle Scholar
  45. 45.
    Viney R, Lancsar E, Louviere J. Discrete choice experiments to measure consumer preferences for health and healthcare. Pharmacoeconomics Outcomes Res 2002; 2(4): 89–101Google Scholar
  46. 46.
    Bridges J, Onukwugha E, Johnson F, et al. Patient preference methods: a patient centered evaluation paradigm. ISPOR Connections 2007; 13(6): 4–7Google Scholar
  47. 47.
    Lee J, Bridges J, Shockney L. Can pharmacoeconomics and outcomes research contribute to the empowerment of women affected by breast cancer? Exp Rev Pharmacoeconomics Outcomes Res 2008 Feb; 8(1): 73–9CrossRefGoogle Scholar

Copyright information

© Adis Data Information BV 2008

Authors and Affiliations

  • John F.P. Bridges
    • 1
    • 2
  • Elizabeth T. Kinter
    • 1
  • Lillian Kidane
    • 1
  • Rebekah R. Heinzen
    • 1
  • Colleen McCormick
    • 1
    • 3
  1. 1.Johns Hopkins Bloomberg School of Public HealthBaltimoreUSA
  2. 2.Center for Medicine in the Public Interest (CMPI)New YorkUSA
  3. 3.Department of Gynecology and ObstetricsThe Johns Hopkins Hospital, Kelly Gynecologic Oncology ServiceBaltimoreUSA
  4. 4.Department of Health Policy and ManagementJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA

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