Abstract
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. Method 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. ResultsThe 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. ConclusionsThese 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.
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