Purpose: Knee osteoarthritis (OA) is a leading cause of disability among older adults. Considerable efforts are currently being directed at developing efficient trial designs to study the efficacy of disease modifying drugs (DMOADs). As part of this effort, an understanding of patients' preferences for DMOADs is needed.
Methods: We administered a conjoint analysis survey to a convenience sample of 304 patients attending outpatient clinics. The survey was composed of 4 attributes each having 3 levels: (1) administration (pill, injection (SC), infusion (IV)), (2) benefit (prevents progression in 40%, 60%, or 80%), (3) risk (mild: < 1 week and reversible, moderate: (1-2 weeks and requires treatment, serious: requires hospitalization), and (4) cost (easy, somewhat, hard to afford). The survey included 12 random choice tasks each with 2 medications and a “None” option. We performed Latent Class Segmentation analysis and simulations to estimate preferences for 4 options: Best Case (most favored levels), Worst Case (least favored levels), SC (SC, lowest benefit, mid-level risk, mid-level cost), IV (same as previous except IV).
Results: Segmentation analysis revealed 4 groups. The relative importances of the attributes, given the levels specified, are presented in Table 1. Group 1 (5%) do not want to perform injections and only consider DMOADs under the Best Case scenario; Group 2 (19.4%) are most influenced by risk and fewer prefer DMOADs under more realistic scenarios; Group 3 (16.4%) consistently reject DMOADs, and Group 4 (59.2%) strongly prefer DMOADs and are willing to accept substantial risk to prevent progression of OA.
Conclusion: As indicated by the estimates provided for the total study population, aggregating choice data may be misleading. Segmentation analysis of conjoint data generates more informative estimates which can be used to plan for future therapies.
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