Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

Towards Patient-Centered Care for Depression

Conjoint Methods to Tailor Treatment Based on Preferences


Background: Although antidepressants and counseling have been shown to be effective in treating patients with depression, non-treatment or under-treatment for depression is common, especially among the elderly and minorities. Previous work on patient preferences has focused on medication versus counseling, but less is known about the value that patients place on attributes of medication and counseling.

Objective: To examine, using conjoint analysis, the relative importance of various attributes of depression treatment at the group level as well as to determine the range of individual-level relative preference weights for specific depression treatment attributes. In addition, to predict what modifications in treatment characteristics are associated with a change in the stated preferred alternative.

Methods: A total of 86 adults who participated in an internet-based panel responded to an online discrete-choice task about depression treatment. Participants chose between medication and counseling based on choice sets presented first for a ‘mild depression’ scenario and then for a ‘severe depression’ scenario. Participants were given 18 choice sets that varied for medication based on type of side effect (nausea, dizziness, and sexual dysfunction) and severity of side effect (mild, moderate, and severe); and for counseling based on frequency of counseling sessions (once per week or every other week) and location of the sessions (mental health professional’s office, primary-care doctor’s office, or office of a spiritual counselor).

Results: Treatment type (counseling vs medication) appeared to be more important in driving treatment choice than any specific attribute that was studied; specifically, counseling was preferred by most of the respondents. After treatment type, location and frequency of treatment were important considerations. Preferred attributes were similar in both the mild and severe depression scenarios. Side effect severity appeared to be most important in driving treatment choice compared with the other attributes studied. Individual-level relative preferences for treatment type revealed a distribution that was roughly bimodal; 27 participants had a strong preference for counseling and 14 had a strong preference for medication.

Conclusions: Estimating individual-level preferences for treatment type allowed us to see the variability in preferences and determine which participants had a strong affinity for medication or counseling. We found that participants preferred counseling over medication, avoided options with severe side effects, and wanted to be seen in the primary-care doctor’s office as opposed to other venues.

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

Table I
Table II
Fig. 1
Table III
Table IV


  1. 1.

    Institute of Medicine. Crossing the quality chasm: a new health system for the 21st century. Washington, DC: National Academies Press, 2001

  2. 2.

    Committee on Crossing the Quality Chasm: Adaptation to Mental Health and Addictive Disorders. Improving the quality of care for mental and substance-use conditions: quality chasm series. Washington, DC: National Academies Press, 2006

  3. 3.

    Bridging science and service: a report by the National Advisory Mental Health Council’s Clinical Treatment and Services Research Workgroup. National Institutes of Health, National Institute of Mental Health, 1998 [online]. Available from URL: http://wwwapps.nimh.nih.gov/ ecb/archives/nimhbridge.pdf [Accessed 2010 Mar 24]

  4. 4.

    Cooper LA, Gonzales JJ, Gallo JJ, et al. The acceptability of treatment for depression among African-American and White primary care patients. Med Care 2003; 41: 479–89

  5. 5.

    Glass C, Arnkoff D, Shapiro S. Expectations and preferences. Psychotherapy 2001; 38: 455–61

  6. 6.

    Swift J, Callahan J. The impact of client treatment preferences on outcome: a meta-analysis. J Clin Psychol 2009; 65(4): 368–81

  7. 7.

    Ryan P, Lauver DR. The efficacy of tailored interventions. J Nurs Scholarsh 2002; 34: 331–7

  8. 8.

    Bogner HR, Lin JY, Morales KH. Patterns of early adherence to the antidepressant citalopram among primary care patients: the PROSPECT study. Int J Psychiatry Med 2006; 36(1): 103–19

  9. 9.

    Nutting PA, Rost K, Dickinson M, et al. Barriers to initiating depression treatment in primary care practice. J Gen Int Med 2002; 17: 103–11

  10. 10.

    Switzer J, Wittink M, Karsch BB, et al. “Pull yourself up by your bootstraps”: a response to depression in older adults. Qual Health Res 2006; 16(9): 1207–16

  11. 11.

    Wittink MN, Dahlberg B, Biruk C, et al. How older adults combine medical and experiential notions of depression. Qual Health Res 2008 Sep; 18(9): 1174–83

  12. 12.

    Thompson J, Scott N. Counseling service features: elders’ preferences and utilization. Clin Gerontol 1991; (11): 39-46

  13. 13.

    Dwight-Johnson M, Unützer J, Sherbourne C, et al. Can quality improvement programs for depression in primary care address patient preferences for treatment? Med Care 2001; 39(9): 934–44

  14. 14.

    Bedi N, Chilvers C, Churchill R, et al. Assessing effectiveness of treatment of depression in primary care: partially randomised preference trial. Br J Psychiatry 2000; 177: 312–8

  15. 15.

    Rokke PD, Tomhave JA, Jocic Z. The role of client choice and target selection in self-management therapy for depression in older adults. Psychol Aging 1999 Mar; 14(1): 155–69

  16. 16.

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

  17. 17.

    Anderson N. Functional measurement and psycho-physical judgement. Psychology Rev 1977; 77: 153–70

  18. 18.

    Louviere JJ. Conjoint analysis modelling of stated preferences: a review of theory, methods, recent developments and external validity. J Transport Econ Policy 1988; 22(1): 9–119

  19. 19.

    Wardman M. A comparison of revealed preference and stated preference models. J Transport Econ Policy 1988; 22: 71–91

  20. 20.

    Opaluch J, Swallow S, Weaver T, et al. Evaluating impacts from noxious facilities: including public preferences in current siting mechanisms. J Environ Econ Manage 1993; (24): 41-59

  21. 21.

    Adamowicz W, Louviere J, Williams M. Combining revealed preference and stated preference methods for valuing environmental amenities. J Environ Econ Manage 1994; 6: 271–92

  22. 22.

    Train K, Sonnier G. Mized logit with bounded distributions of correlated partworths. In:Scarpa R, Alberini A, editors. Applications of simulation methods in environmental and resource economics. Dordrecht: Springer, 2005

  23. 23.

    Wittink DR, Cattin P. Commercial use of conjoint analysis: an update. J Market 1981; 53: 91–6

  24. 24.

    Green PE, Srinivasan V. Conjoint analysis in marketing: new developments with implications for research and practice. J Market 1990; 54(4): 3–19

  25. 25.

    Ryan M, Farrar S. Using conjoint analysis to elicit preferences for health care. BMJ 2000 Jun 3; 320(7248): 1530–3

  26. 26.

    Ryan M, Gerard K. Using discrete choice experiments to value health care programmes: current practice and future reflections. Appl Health Econ Health Policy 2003; 2(1): 55–64

  27. 27.

    Bridges J, Kinter E, Kidane L, et al. Things are looking up since we started listening to patients: trends in the application of conjoint analysis in health 1982–2007. Patient 2008; 1(4): 273–82

  28. 28.

    Ross M-A, Avery AJ, Foss AJE. Views of older people on cataract surgery options: an assessment of preferences by conjoint analysis. Qual Saf Health Care 2003; 12: 13–7

  29. 29.

    Meister H, Lausberg I, Kiessling J. Identifying the needs of elderly, hearing-impaired persons: the importance and utility of hearing aid attributes. Eur Arch Otorhinolaryngol 2002; 259(10): 531–4

  30. 30.

    Ratcliffe J, Buxton M, McGarry T, et al. Patients’ preferences for characteristics associated with treatments for osteoarthritis. Rheumatology 2004; 43: 337–45

  31. 31.

    van den Berg B, Van Dommelen P, Stam P, et al. Preferences and choices for care and health insurance. Soc Sci Med 2008 Jun; 66(12): 2448–59

  32. 32.

    Oudhoff JP, Timmermans DR, Knol DL, et al. Prioritising patients on surgical waiting lists: a conjoint analysis study on the priority judgments of patients, surgeons, occupational physicians, and general practitioners. Soc Sci Med 2007 May; 64(9): 1863–75

  33. 33.

    Ryan M, Hughes J. Using conjoint analysis to assess women’s preferences for miscarriage management. Health Econ 1998; 6(3): 261–73

  34. 34.

    Flach SD, Diener A. Eliciting patients’ preferences for cigarette and alcohol cessation: an application of conjoint analysis. Addict Behav 2004; 29: 791–9

  35. 35.

    Shumway M, Saunders T, Shern D, et al. Preferences for schizophrenia treatment outcomes among public policy makers, consumers, families, and providers. Psychiatr Serv 2003; 54: 1124–8

  36. 36.

    Dwight-Johnson M, Lagomasino IT, Aisenberg E, et al. Using conjoint analysis to assess depression treatment preferences among low-income latinos. Psychiatr Serv 2004; 55(8): 934–6

  37. 37.

    Cooper LA, Brown C, Thi Vu H, et al. Primary care patients’ opinions regarding the importance of various aspects of care for depression. Gen Hosp Psychiatry 2000; 22(3): 163–73

  38. 38.

    Jonathan Baron’s questionnaire studies [online]. Available from URL: http://www.psych.upenn.edu/∼baron/q.htm [Accessed 2010 Apr 23]

  39. 39.

    Baron J. Thinking and deciding. 3rd ed. New York: Cambridge University Press, 2000

  40. 40.

    Gurmankin AD, Baron J, Hershey JC, et al. The role of physicians’ recommendations in medical treatment decisions. Med Decis Making 2002; 22: 262–71

  41. 41.

    Gurmankin LA, Baron J. How bad is a 10% chance of losing a toe? Judgments of probabilistic conditions by doctors and laypeople. Mem Cognit 2005; 33(8): 1399–406

  42. 42.

    Baron J, Asch DA, Fagerlin A, et al. Effect of assessment method on the discrepancy between judgments of health disorders people have and do not have: a web study. Med Decis Making 2003 Sep–Oct; 23(5): 422–34

  43. 43.

    Gurmankin AD, Baron J, Armstrong K. Intended message versus message received in hypothetical physician risk communications: exploring the gap. Risk Anal 2004; 24: 1337–47

  44. 44.

    Kuhfeld W. Marketing research methods for SAS: experimental design, choice, conjoint, and graphical techniques. SAS 9.1 ed. Cary (NC): SAS Institute Inc., 2005

  45. 45.

    Schulberg HC, Katon W, Simon GE, et al. Treating major depression in primary care: an update of the Agency for Health Care Policy and Research Practice Guidelines. Arch Gen Psychiatry 1998; 55: 1121–7

  46. 46.

    Johnson R, Orme B. How many questions should you ask in choice-based conjoint studies? [Sawtooth Software research paper series]. Beaver Creek (CO): Sawtooth Software Inc., Advanced Research Techniques Forum, 1996

  47. 47.

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

  48. 48.

    Marshall P, Bradlow ET. A unified approach to conjoint analysis models. J Am Stat Assoc 2002; 97: 674–82

  49. 49.

    Gum A, Areán P, Hunkeler E, et al. Depression treatment preferences in older primary care patients. Gerontologist 2006; 46: 14–22

  50. 50.

    Jaycox L, Asarnow J, Sherbourne C, et al. Adolescent primary care patients’ preferences for depression treatment. Adm Policy Ment Health 2006; 33(2): 198–207

  51. 51.

    Givens J. Ethnicity and preferences for depression treatment. Gen Hosp Psychiatry 2007; 29: 254–63

  52. 52.

    Wind J, Green P, Shifflet D, et al. Courtyard by Marriott: designing a hotel facility with consumer-based marketing models. Interfaces 1989; 19 (Jan–Feb): 25–47

  53. 53.

    Vavra T, Green P, Krieger A. Evaluating EZ-pass. Mark Res 1999; 11: 5–16

  54. 54.

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

  55. 55.

    Cunningham CE, Buchanan D, Deal K. Modelling patient-centered children’s health services using choice-based conjoint hierarchical Bayes. Paper presented at 10th Annual Sawtooth Software Conference Proceedings; 2004 Apr 15–17; San Antonio (TX)

  56. 56.

    Cunningham CE, Deal K, Rimas H, et al. Modeling the information preferences of parents of children with mental health problems: a discrete choice conjoint experiment. J Abnorm Psychol 2008; 36(36): 1123–38

Download references


Dr Wittink was supported by a National Institute of Mental Health (NIMH) Mentored Patient-Oriented Research Career Development Award (MH19931) and an NIMH sponsored grant entitled ‘Developing Methods for Tailoring Depression Treatment to Older Adults 80’ (R34 MH085906).

The authors have no conflicts of interest that are directly relevant to the content of this study.

Dr Wittink also wishes to thank her late father, Dr Dick R. Wittink, a marketing and econometrics researcher, for introducing her to conjoint analysis.

Author information

Correspondence to Dr Marsha N. Wittink.

Electronic supplementary material

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Wittink, M.N., Cary, M., TenHave, T. et al. Towards Patient-Centered Care for Depression. Patient-Patient-Centered-Outcome-Res 3, 145–157 (2010). https://doi.org/10.2165/11530660-000000000-00000

Download citation


  • Counseling Session
  • Choice Task
  • Conjoint Analysis
  • Depression Treatment
  • Mild Depression