Research article

A systematic review of models to predict recruitment to multicentre clinical trials

Katharine D Barnard^*, Louise Dent and Andrew Cook

• * Corresponding author: Katharine D Barnard k.barnard@soton.ac.uk

Author Affiliations

National Institute for Health Research Evaluation, Trials and Studies Coordinating Centre (NETSCC), University of Southampton, UK

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BMC Medical Research Methodology 2010, 10:63 doi:10.1186/1471-2288-10-63

Published: 6 July 2010

Abstract

Background

Less than one third of publicly funded trials managed to recruit according to their original plan often resulting in request for additional funding and/or time extensions. The aim was to identify models which might be useful to a major public funder of randomised controlled trials when estimating likely time requirements for recruiting trial participants. The requirements of a useful model were identified as usability, based on experience, able to reflect time trends, accounting for centre recruitment and contribution to a commissioning decision.

Methods

A systematic review of English language articles using MEDLINE and EMBASE. Search terms included: randomised controlled trial, patient, accrual, predict, enrol, models, statistical; Bayes Theorem; Decision Theory; Monte Carlo Method and Poisson. Only studies discussing prediction of recruitment to trials using a modelling approach were included. Information was extracted from articles by one author, and checked by a second, using a pre-defined form.

Results

Out of 326 identified abstracts, only 8 met all the inclusion criteria. Of these 8 studies examined, there are five major classes of model discussed: the unconditional model, the conditional model, the Poisson model, Bayesian models and Monte Carlo simulation of Markov models. None of these meet all the pre-identified needs of the funder.

Conclusions

To meet the needs of a number of research programmes, a new model is required as a matter of importance. Any model chosen should be validated against both retrospective and prospective data, to ensure the predictions it gives are superior to those currently used.