Beyond prediction: an evidenced-based framework for assessing site selection decisions.

BACKGROUND: Clinical trial success depends on selecting appropriate sites, yet current selection approaches, often predictive and profile-based, exhibit systematic problems where sites with similar profiles demonstrate inconsistent performance. METHOD: Based on interviews with sponsors, CROs, and sites, along with a literature review, we developed a framework that explains, rather than predicts, site performances. It distinguishes between site inputs (resources and operating environment), dynamic capabilities (coordinated site-level abilities), and outputs (performance metrics). RESULTS: Analysis revealed a circular reasoning problem in feasibility projections where sites self-assess their own performance potential. Real-world examples demonstrate that practitioners are intuitively applying our model's principles, indicating readiness for more systematic framework. CONCLUSION: Our model's framework offers actionable insights for improving both individual selection decisions and systematic selection processes, supporting from prospective site selection to ongoing performance diagnosis throughout the trial lifecycle.