Optimizing Patient Recruitment in Global Clinical Trials using Nature-Inspired Metaheuristics

Mitchell Aaron Schepps, Weng Kee Wong, Matt Austin, Volodymyr Anisimov

研究成果: Article同行評審


A common problem seen in the ineffective execution of global multicenter trials is the frequent inability to recruit a sufficient number of patients. A myriad of barriers to recruiting enough patients timely exist and may include practical limits on the number of recruiting sites imposed by the various countries, as well as administrative, cost and unanticipated issues. The Poisson-gamma recruitment model is a widely accepted statistical tool used to predict and track recruitment at different levels of a trial and make inference. An optimal recruitment plan can be designed using mathematical optimization, however, the search is complicated with multiple nonlinear objectives and constraints that arise from regulatory and budgetary considerations. We review nature-inspired metaheuristic algorithms which are powerful, flexible, general purpose optimization tools and demonstrate their capability and utility in optimizing complex recruitment designs for global clinical trials using the Poisson-gamma model as an exemplary model. This research opens up new avenues for improving the efficiency and effectiveness of patient recruitment in clinical trials, thereby potentially accelerating the development of new medical treatments.

期刊Statistics in Biopharmaceutical Research
出版狀態Accepted/In press - 2024

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 藥學科學


深入研究「Optimizing Patient Recruitment in Global Clinical Trials using Nature-Inspired Metaheuristics」主題。共同形成了獨特的指紋。