A systematic review of models to predict recruitment to multicentre clinical trials View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-12

AUTHORS

Katharine D Barnard, Louise Dent, Andrew Cook

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. More... »

PAGES

63

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/1471-2288-10-63

DOI

http://dx.doi.org/10.1186/1471-2288-10-63

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1043640186

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20604946


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