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Concept End Points Informing Design Considerations for Confirmatory Clinical Trials in Osteoarthritis

Yura Kim 1, Gregory Levin 1, Nikolay P Nikolov 1, Robert Abugov 1, Rebecca Rothwell 1

1US Food and Drug Administration, Silver Spring, Maryland.

Arthritis Care Res (Hoboken). 2022 Jul;74(7):1154-1162. doi: 10.1002/acr.24549. Epub 2022 Apr 17.


Objective: There is an unmet need for therapies that target the underlying pathophysiology of osteoarthritis (OA). However, defining appropriate measures for clinical trials of such therapies is challenging. Our objective was to propose concept clinical end points that directly capture clinical benefit in this setting and evaluate the feasibility of their use.

Methods: This analysis used the multicenter, longitudinal, observational Osteoarthritis Initiative (OAI) database. OAI participants primarily had knee OA, with follow-up of up to 9 years and assessments of joints, surgical interventions, performance outcomes, and patient-reported outcomes. We examined this data set to identify existing outcome measures of direct clinical benefit. We evaluated the feasibility of conducting trials using these candidate end points by estimating incidence rates and resulting required sample sizes and study durations in time-to-event analyses.

Results: We identified candidate end points based on total knee replacement (TKR) and composite end points defined by TKR and conservative thresholds of patient-reported outcomes of pain and function. Using time to TKR as an end point, a study with an average follow-up time of 3 years requires approximately 3,000 to 18,000 subjects, depending on effect size. Alternatively, for a composite end point, such as "time to TKR or severe pain or severely impaired functioning," the required sample sizes ranged from approximately 2,000 to 11,000 for a 3-year study.

Conclusion: The proposed concept end points can reliably and feasibly evaluate the effectiveness of therapies for this unmet need. In particular, the composite end point approach can substantially reduce sample sizes (up to approximately 40%) compared to the use of TKR alone.

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