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

BMC Med Res Methodol. 2010 Jul 6;10:63. doi: 10.1186/1471-2288-10-63.


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.

Publication types

  • Review
  • Systematic Review

MeSH terms

  • Bayes Theorem
  • Humans
  • Models, Statistical*
  • Monte Carlo Method
  • Multicenter Studies as Topic
  • Patient Selection*
  • Poisson Distribution
  • Randomized Controlled Trials as Topic*
  • Time Factors