title: A systematic review of models to predict recruitment to multicentre clinical trials.
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, 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.
authors.affiliation: National Institute for Health Research Evaluation, Trials and Studies Coordinating Centre, University of Southampton, UK. k.barnard@soton.ac.uk
authors.forename: Katharine D
authors.initials: KD
authors.lastname: Barnard
authors.forename: Louise
authors.initials: L
authors.lastname: Dent
authors.forename: Andrew
authors.initials: A
authors.lastname: Cook
mesh_terms: 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