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