Modelling, prediction and adaptive adjustment of recruitment in multicentre trials

What is it about?

This paper is focused on statistical modelling, prediction and adaptive adjustment of patient recruitment in multicentre clinical trials. We consider a recruitment model, where patients arrive at different centres according to Poisson processes, with recruitment rates viewed as a sample from a gamma distribution. A statistical analysis of completed studies is provided and properties of a few types of parameter estimators are investigated analytically and using simulation. The model has been validated using many real completed trials. A statistical technique for predictive recruitment modelling for ongoing trials is developed. It allows the prediction of the remaining recruitment time together with confidence intervals using current enrolment information, and also provision of an adaptive adjustment of recruitment by calculating the number of additional centres required to accomplish a study up to a certain deadline with a pre-specified probability. Results are illustrated for different recruitment scenarios.

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Why is it important?

Predicting recruitment and the recruitment time is one of the key decision variables in the design stage of a clinical trial. Existing techniques for recruitment planning are mainly deterministic and do not account for the various uncertainties in input information and stochastic fluctuations of the recruitment in time. Therefore, a large proportion of trials fail to complete by the enrolment deadline. Thus, the development of statistical methodology is a hugely important task.

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