Article (Scientific journals)
Linear mixed-effects models for central statistical monitoring of multicenter clinical trials.
Desmet, Lieven; Venet, D.; Doffagne, E. et al.
2014In Statistics in Medicine, 33 (30), p. 5265-5279
Peer Reviewed verified by ORBi
 

Files


Full Text
Preprint_DesmetEtAl_SIM_2014.pdf
Author preprint (335.67 kB)
Request a copy

All documents in ORBi are protected by a user license.

Send to



Details



Keywords :
multicenter clinical trial
Abstract :
[en] 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 & Sons, Ltd.
Disciplines :
Mathematics
Human health sciences: Multidisciplinary, general & others
Author, co-author :
Desmet, Lieven
Venet, D.
Doffagne, E.
Timmermans, Catherine ;  International Drug Development Institute (IDDI)
Burzykowski, T.
Legrand, Catherine;  Université Catholique de Louvain - UCL
Buyse, M.
Language :
English
Title :
Linear mixed-effects models for central statistical monitoring of multicenter clinical trials.
Publication date :
2014
Journal title :
Statistics in Medicine
ISSN :
0277-6715
eISSN :
1097-0258
Publisher :
John Wiley & Sons, Hoboken, United States - New Jersey
Volume :
33
Issue :
30
Pages :
5265-5279
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 14 February 2019

Statistics


Number of views
41 (2 by ULiège)
Number of downloads
0 (0 by ULiège)

Scopus citations®
 
23
Scopus citations®
without self-citations
15
OpenCitations
 
27

Bibliography


Similar publications



Contact ORBi