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

Published online

Journal Article (Review)

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.

Full Text

Duke Authors

Cited Authors

  • Barnard, KD; Dent, L; Cook, A

Published Date

  • July 6, 2010

Published In

Volume / Issue

  • 10 /

Start / End Page

  • 63 -

PubMed ID

  • 20604946

Pubmed Central ID

  • 20604946

Electronic International Standard Serial Number (EISSN)

  • 1471-2288

Digital Object Identifier (DOI)

  • 10.1186/1471-2288-10-63

Language

  • eng

Conference Location

  • England