Interim recruitment prediction for multi-centre clinical trials

TL;DR

This paper presents a Bayesian framework using inhomogeneous Poisson processes to improve recruitment predictions in multi-centre clinical trials, addressing limitations of existing models with narrow prediction intervals.

Contribution

It introduces a flexible, decay-based recruitment model with Bayesian averaging, enhancing prediction accuracy and robustness in multi-centre trial monitoring.

Findings

Model accurately predicts recruitment trends in oncology trials.

Method outperforms existing models in prediction intervals.

Robust to model misspecification and adaptable to various recruitment patterns.

Abstract

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.