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This paper focuses on statistical modelling and prediction of patientrecruitment in clinical trials accounting for patients dropout. The recruitmentmodel is based on a Poisson-gamma model introduced by Anisimov and Fedorov(2007), where the patients arrive at different centres according to Poissonprocesses with rates viewed as gamma-distributed random variables. Each patientcan drop the study during some screening period. Managing the dropout processis of a major importance but data related to dropout are rarely correctlycollected. In this paper, a few models of dropout are proposed. The techniquefor estimating parameters and predicting the number of recruited patients overtime and the recruitment time is developed. Simulation results confirm theapplicability of the technique and thus, the necessity to account for patientsdropout at the stage of forecasting recruitment in clinical trials.

Modelling and forecasting patient recruitment in clinical trials with patients' dropout