Linear mixed‐effects models for central statistical monitoring of multicenter clinical trials. (12th September 2014)

• Record Type: Journal Article
• Authors: Desmet, L.; Venet, D.; Doffagne, E.; Timmermans, C.; Burzykowski, T.; Legrand, C.; Buyse, M.; Friede, Tim; Henderson, Robin; Hougaard, Philip
• Is Part Of: Statistics in medicine. Volume 33:Number 30(2014)
• Page Start: 5265
• Page End: 5279
• Publication Date: 2014-09-12
• Subjects: Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.6294
• Languages: English
• ISSNs: 0277-6715

Abstract

<abstract abstract-type="main" id="sim6294-abs-0001"> <title> <x xml:space="preserve">Abstract</x> </title> <p id="sim6294-para-0001">Multicenter studies are widely used to meet accrual targets in clinical trials. Clinical data monitoring is required to ensure the quality and validity of the data gathered across centers. One approach to this end is central statistical monitoring, which aims at detecting atypical patterns in the data by means of statistical methods. In this context, we consider the simple case of a continuous variable, and we propose a detection procedure based on a linear mixed‐effects model to detect location differences between each center and all other centers. We describe the performance of the procedure as a function of contamination rate and signal‐to‐noise ratio. We investigate the effect of center size and variance structure and illustrate the use of the procedure using data from two multicenter clinical trials. Copyright © 2014 John Wiley &amp; Sons, Ltd.</p> </abstract>