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Modeling restricted enrollment and optimal cost-efficient design in multicenter clinical trials

Vladimir AnisimovMatthew Austin
Dec 2022
Design and forecasting of patient enrollment is among the greatest challengesthat the clinical research enterprize faces today, as inefficient enrollmentcan be a major cause of drug development delays. Therefore, the development ofthe innovative statistical and artificial intelligence technologies forimproving the efficiency of clinical trials operation are of the imperativeneed. This paper is describing further developments in the innovativestatistical methodology for modeling and forecasting patient enrollment. Theunderlying technique uses a Poisson-gamma enrollment model developed byAnisimov & Fedorov in the previous publications and is extended here toanalytic modeling of the enrollment on country/region level. A new analytictechnique based on the approximation of the enrollment process incountry/region by a Poisson-gamma process with aggregated parameters isdeveloped. Another innovative direction is the development of the analytictechnique for modeling the enrollment under some restrictions (enrollment capsin countries). Some discussion on using historic trials for better predictionof the enrollment in the new trials is provided. These results are used forsolving the problem of optimal trial cost-efficient enrollment design: find anoptimal allocation of sites/countries that minimizes the global trial costgiven that the probability to reach an enrollment target in time is no lessthan some prescribed probability. Different techniques to find an optimalsolution for high dimensional optimization problem for the cases ofunrestricted and restricted enrollment and for a small and large number ofcountries are discussed.