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

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.

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).

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.

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.