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
1-8
Barnard, Katherine
1ade2840-48a4-4bb3-b564-0a058df8297f
Dent, Louise
8b827763-d839-4b4b-bbf2-358a84110294
Cook, Andrew
ab9c7bb3-974a-4db9-b3c2-9942988005d5
July 2010
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), .
(doi:10.1186/1471-2288-10-63).
(PMID:20604946)
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.
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1471-2288-10-63.pdf
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Published date: July 2010
Keywords:
rct, prediction, model
Organisations:
Faculty of Medicine
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Local EPrints ID: 366784
URI: http://eprints.soton.ac.uk/id/eprint/366784
ISSN: 1471-2288
PURE UUID: 28270c24-1a65-4242-b87f-d5bdcc493219
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Date deposited: 09 Jul 2014 14:28
Last modified: 15 Mar 2024 03:28
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Author:
Katherine Barnard
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