A systematic review of models to predict recruitment to multicentre clinical trials.
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, enroll, 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.
Barnard
Katharine D
KD
National Institute for Health Research Evaluation, Trials and Studies Coordinating Centre, University of Southampton, UK. k.barnard@soton.ac.uk
Dent
Louise
L
Cook
Andrew
A
D001499:Bayes Theorem; D006801:Humans; D015233:Models, Statistical; D009010:Monte Carlo Method; D015337:Multicenter Studies as Topic; D018579:Patient Selection; D016012:Poisson Distribution; D016032:Randomized Controlled Trials as Topic; D013997:Time Factors