Document Detail


Modelling, prediction and adaptive adjustment of recruitment in multicentre trials.
MedLine Citation:
PMID:  17639505     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This paper is focused on statistical modelling, prediction and adaptive adjustment of patient recruitment in multicentre clinical trials. We consider a recruitment model, where patients arrive at different centres according to Poisson processes, with recruitment rates viewed as a sample from a gamma distribution. A statistical analysis of completed studies is provided and properties of a few types of parameter estimators are investigated analytically and using simulation. The model has been validated using many real completed trials. A statistical technique for predictive recruitment modelling for ongoing trials is developed. It allows the prediction of the remaining recruitment time together with confidence intervals using current enrolment information, and also provision of an adaptive adjustment of recruitment by calculating the number of additional centres required to accomplish a study up to a certain deadline with a pre-specified probability. Results are illustrated for different recruitment scenarios.
Authors:
Vladimir V Anisimov; Valerii V Fedorov
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Publication Detail:
Type:  Comparative Study; Journal Article    
Journal Detail:
Title:  Statistics in medicine     Volume:  26     ISSN:  0277-6715     ISO Abbreviation:  Stat Med     Publication Date:  2007 Nov 
Date Detail:
Created Date:  2007-10-17     Completed Date:  2008-01-24     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8215016     Medline TA:  Stat Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  4958-75     Citation Subset:  IM    
Affiliation:
GlaxoSmithKline, Research Statistics Unit, NFSP-South, Third Avenue, Harlow, Essex CM19 5AW, UK. vladimir.v.anisimov@gsk.com
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MeSH Terms
Descriptor/Qualifier:
Clinical Trials as Topic / methods*
Computer Simulation
Humans
Models, Statistical*
Monte Carlo Method
Multicenter Studies as Topic / methods*
Patient Selection*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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