Open Access Open Access  Restricted Access Subscription Access


Basem Al-Omari, Julius Sim, Peter Croft, Martin Frisher


Rationale, aims and objectives

Patient preferences are an important part of optimizing the pharmacological treatment of osteoarthritis (OA). Recent choice experiments have explored this issue using two types of conjoint analysis: choice-based conjoint analysis (CBCA) and adaptive conjoint analysis (ACA). The aim of this study was to examine the feasibility of using adaptive choice-based conjoint analysis (ACBCA) methods to determine patient preferences for pharmacological treatment of OA. The specific outcomes were patient evaluations of a) eight attributes in an ACBCA task, b) the computer skills required to complete the task, and c) the perceived utility of the results.



Participants were drawn from members of a Research Users’ Group (RUG) who had been diagnosed with osteoarthritis. Participants took part in two feasibility studies. In the first feasibility study, four RUG members critically examined the implementation of a computerized ACBCA task. In the second feasibility study, 11 RUG members completed an ACBCA task on medication preferences for osteoarthritis. The ACBCA task was evaluated by a set of self-completed questions and through semi-structured interviews.



The first feasibility study helped to shape the design and contents of the ACBCA task. In the second feasibility study, no participants reported the ACBCA task to be hard to read or understand. Most participants agreed that the task was adjusting appropriately as the session proceeded and that it helped them in making decisions about preferences. Older patients and patients with little computer experience appeared to find no substantial challenges in using this interactive computer-based technique.



These studies indicate that, with the involvement of patients, face and content validity of an ACBCA task can be achieved through a developmental process taking account of participants’ requirements. 


Adaptive choice-based conjoint analysis, osteoarthritis, patient preferences, person-centered healthcare, pharmaceutical treatment

Full Text:



Ryan, M. & Gerard, K. (2003). Using discrete choice experiments to value health care programmes: current practice and future research reflections. Applied Health Economics and Health Policy 2 (1) 55-64.

Ryan, M. & Farrar, S. (2000). Using conjoint analysis to elicit preferences for health care. British Medical Journal (Clinical Research Edition.) 320 (7248) 1530-1533.

Farrar, S., Ryan, M., Ross, D. & Ludbrook, A. (2000). Using discrete choice modelling in priority setting: an application to clinical service developments. Social Science and Medicine 50, 63-75.

Fraenkel, L., Bogardus, S.T., Concato, J. & Wittink, D.R. (2004). Treatment options in knee osteoarthritis: the patient's perspective. Archives of Internal Medicine 164 (12) 1299-1304.

Fraenkel, L., Wittink, D.R., Concato, J. & Fried, T. (2004). Are preferences for cyclooxygenase-2 inhibitors influenced by the certainty effect? Journal of Rheumatology 31 (3) 591-593.

Fraenkel, L., Wittink, D.R., Concato, J. & Fried, T. (2004). Informed choice and the widespread use of antiinflammatory drugs. Arthritis and Rheumatism 51 (2) 210-214.

Fraenkel, L. & Fried, T. (2008). If you want patients with knee osteoarthritis (OA) to exercise: tell them about NSAIDS. The Patient 1 (1) 21-26.

Ratcliffe, J., Buxton, M., McGarry, T., Sheldon, R. & Chancellor, J. (2004). Patients' preferences for characteristics associated with treatments for osteoarthritis. Rheumatology 43 (3) 337-345.

Chang, J., Kauf, T.L., Mahajan, S., Jordan, J.M., Kraus, V.B., Vail, T.P., Reed, S.D., Omar, M.A., Kahler, K.H. & Schulman, K.A. (2005). Impact of disease severity and gastrointestinal side effects on the health state preferences of patients with osteoarthritis. Arthritis & Rheumatism 52 (8) 2366-2375.

Orme, B. (2009). Which Conjoint Method Should I Use? Available from: [Accessed: 25th November 2014].

Sawtooth. (2009). ACBC Technical Paper. Available from: [Accessed: 25th November 2014].

Byrne, M.M., Souchek, J., Richardson, M. & Suarez-Almazor, M. (2006). Racial/ethnic differences in preferences for total knee replacement surgery. Journal of Clinical Epidemiology 59 (10) 1078-1086.

Kerlinger, F.N. & Lee, H.B. (1999). Foundations of Behavioral Research. 4th edn. Belmont, CA: Cengage Learning.

Rochon, D., Eberth, J.M., Fraenkel, L., Volk, R.J. & Whitney, S.N. (2012). Elderly patients' experiences using adaptive conjoint analysis software as a decision aid for osteoarthritis of the knee. Health Expectations 17 (6) 840-851.

Dobney Corporation. (2011). Flavours or Types of Conjoint Analysis. Available from: [Accessed: 25th November 2014].

Pieterse, A.H., Berkers, F., Baas-Thijssen, M.C., Marijnen, C.A. & Stiggelbout, A.M. (2010). Adaptive Conjoint Analysis as individual preference assessment tool: feasibility through the internet and reliability of preferences. Patient Education and Counseling 78 (2) 224-233.

Green, P. & Srinivasan, V. (1990). Conjoint analysis in marketing: new developments with implications for research and practice. Journal of Marketing 54 (4) 3-19.

Goodare, H. & Lockwood, S. (1999). Involving patients in clinical research. Improves the quality of research. British Medical Journal (Clinical Research Edition.) 319 (7212) 724-725.

Barber, R., Boote, J.D. & Cooper, C.L. (2007). Involving consumers successfully in NHS research: a national survey. Health Expectations 10 (4) 380-391.

Klein, A., Nihalani, K. & Krishnan, K. (2010). A comparison of the validity of interviewer-based and online-conjoint analyses. Journal of Management and Marketing Research 4 (1) 1-15.



  • There are currently no refbacks.