title: Linear mixed-effects models for central statistical monitoring of multicenter clinical trials.
abstract: 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.
authors.affiliation: Institut de Statistique, Biostatistique et Sciences Actuarielles (ISBA), Université catholique de Louvain, Louvain-la-Neuve, Belgium.
authors.forename: L
authors.initials: L
authors.lastname: Desmet
authors.forename: D
authors.initials: D
authors.lastname: Venet
authors.forename: E
authors.initials: E
authors.lastname: Doffagne
authors.forename: C
authors.initials: C
authors.lastname: Timmermans
authors.forename: T
authors.initials: T
authors.lastname: Burzykowski
authors.forename: C
authors.initials: C
authors.lastname: Legrand
authors.forename: M
authors.initials: M
authors.lastname: Buyse
mesh_terms: D015982:Bias; D001699:Biometry / Q000379:methods; D003198:Computer Simulation; D006801:Humans; D016014:Linear Models; D015337:Multicenter Studies as Topic / Q000379:methods*; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio
keywords: contamination rate; error detection; linear mixed-effects model; multicenter clinical trial; signal-to-noise ratio; statistical monitoring