The University of Southampton
University of Southampton Institutional Repository

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

A systematic review of models to predict recruitment to multicentre clinical trials
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.
rct, prediction, model
1471-2288
1-8
Barnard, Katherine
1ade2840-48a4-4bb3-b564-0a058df8297f
Dent, Louise
8b827763-d839-4b4b-bbf2-358a84110294
Cook, Andrew
ab9c7bb3-974a-4db9-b3c2-9942988005d5
Barnard, Katherine
1ade2840-48a4-4bb3-b564-0a058df8297f
Dent, Louise
8b827763-d839-4b4b-bbf2-358a84110294
Cook, Andrew
ab9c7bb3-974a-4db9-b3c2-9942988005d5

Barnard, Katherine, Dent, Louise and Cook, Andrew (2010) A systematic review of models to predict recruitment to multicentre clinical trials. BMC Medical Research Methodology, 10 (63), 1-8. (doi:10.1186/1471-2288-10-63). (PMID:20604946)

Record type: Article

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.

PDF
1471-2288-10-63.pdf - Version of Record
Available under License Other.
Download (306kB)

More information

Published date: July 2010
Keywords: rct, prediction, model
Organisations: Faculty of Medicine

Identifiers

Local EPrints ID: 366784
URI: https://eprints.soton.ac.uk/id/eprint/366784
ISSN: 1471-2288
PURE UUID: 28270c24-1a65-4242-b87f-d5bdcc493219
ORCID for Andrew Cook: ORCID iD orcid.org/0000-0002-6680-439X

Catalogue record

Date deposited: 09 Jul 2014 14:28
Last modified: 06 Jun 2018 12:40

Export record

Altmetrics

Contributors

Author: Katherine Barnard
Author: Louise Dent
Author: Andrew Cook ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×