  We introduce a general framework for monitoring, modelling, and predicting
the recruitment to multi-centre clinical trials. The work is motivated by
overly optimistic and narrow prediction intervals produced by existing
time-homogeneous recruitment models for multi-centre recruitment. We first
present two tests for detection of decay in recruitment rates, together with a
power study. We then introduce a model based on the inhomogeneous Poisson
process with monotonically decaying intensity, motivated by recruitment trends
observed in oncology trials. The general form of the model permits adaptation
to any parametric curve-shape. A general method for constructing sensible
parameter priors is provided and Bayesian model averaging is used for making
predictions which account for the uncertainty in both the parameters and the
model. The validity of the method and its robustness to misspecification are
tested using simulated datasets. The new methodology is then applied to
oncology trial data, where we make interim accrual predictions, comparing them
to those obtained by existing methods, and indicate where unexpected changes in
the accrual pattern occur.
