Accrual Index: A Real-Time Measure of the Timeliness of Clinical Study Enrollment

Background: Achieving timely accrual into clinical research studies remains a challenge for clinical translational research. We developed an evaluation measure, the Accrual Index (AI), normalized for sample size and study duration, using data from the protocol and study management databases. We applied the AI retrospectively and prospectively to assess its utility. Methods: Accrual Target, Projected Time to Accrual Completion (PTAC), Evaluable Subjects, Dates of Recruitment Initiation, Analysis, and Completion were defined. AI is (% Accrual Target accrued/% PTAC elapsed). Changes to recruitment practices were described, and data extracted from study management databases. Results: December 2014 (or final) AI was analyzed for 101 studies initiating recruitment from 2007 to 2014. Median AI was ≥1 for protocols initiating recruitment in 2011, 2013, and 2014. The AI varied widely for studies pre-2013. Studies with AI > 4 utilized convenience samples for recruitment. Data-justified PTAC was refined in 2013-2014 after which the AI range narrowed. Protocol characteristics were not associated with study AI. Conclusion: Protocol AI reflects the relative agreement between accrual feasibility assessment (PTAC), and accrual performance, and is affected by recruitment practices. The AI may be useful in managing accountability, modeling accrual, allocating recruitment resources, and testing innovations in recruitment practices.