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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.

IntroductionTo explore how adaptive choice-based conjoint (ACBC) analysis could contribute to shared decision-making in the treatment of individual patients with osteoarthritis (OA).MethodsIn-depth case study of three individuals randomly selected from patients with OA participating in an ACBC analysis exercise. Eleven members of a research users’ group participated in developing an ACBC task on medication preferences for OA. Individual medication priorities are illustrated by the detailed analysis of ACBC output from three randomly selected patients from the main sample.ResultsThe case study analysis illustrates individual preferences. Participant 1’s priority was avoidance of the four high-risk side effects of medication, which accounted for 90% of the importance of all attributes, while the remaining attributes (expected benefit; way of taking medication; frequency; availability) accounted only for 10% of the total influence. Participant 3 was similar to participant 1 but would accept a high risk of one of the side effects if the medication were available by prescription. In contrast, participant 2’s priority was the avoidance of Internet purchase of medication; this attribute (availability) accounted for 52% of the importance of all attributes.ConclusionsIndividual patients have preferences that likely lead to different medication choices. ACBC has the potential as a tool for physicians to identify individual patient preferences as a practical basis for concordant prescribing for OA in clinical practice. Future research needs to establish whether accurate knowledge of individual patient preferences for treatment attributes and levels translates into concordant behavior in clinical practice.

Background: Patient preferences for pharmaceutical treatment of osteoarthritis have been investigated using Conjoint Analysis. Studies have identified the importance of side effects in determining preferences, but noted that methodological limitations precluded further investigation of additional attributes such as hepatic and renal toxicity.Objective: Following on from a feasibility study of adaptive choice-based conjoint (ACBC) analysis, the aim of this study was to evaluate 8 medication attributes for the pharmaceutical treatment of osteoarthritis (OA).Setting and Participants: Eleven participants were recruited from members of a Research Users’ Group (RUG) who had been diagnosed with osteoarthritis. RUG members individually complete the ACBC task. Main outcome measures: The relative importance of each attribute and the utilities (part-worth) of each level of each attribute were estimated using ACBC built-in Hierarchical Bayes (HB).Results: The combined relative importance of the 4 risk side-effect attributes when selecting osteoarthritis medication (kidney and liver side effects, heart attack and stroke side effects, stomach side effects and addiction) was 66% while the effectiveness attribute accounted for 8% of the relative importance of the medication decision.Conclusions: In this study, the gap between relative importance of 4 side-effect attributes and expected benefit was 66% vs 8%. These preliminary findings indicate that OA patients are most concerned with the avoidance of adverse events and that there is a threshold above which expected benefit has little impact on patients’ medication preferences. The study highlights methodological features of ACBC that may be useful more generally in health services research, but the results must be interpreted in conjunction with the study limitations.

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