title: Accounting for variability in individual hierarchical clinical trial data.
abstract: Meta-analytical approaches have been extensively used to analyze medical data. In most cases, the data come from different studies or independent trials with similar characteristics. However, these methods can be applied in a broader sense. In this paper, we show how existing meta-analytic techniques can also be used as well when dealing with parameters estimated from individual hierarchical data. Specifically, we propose to apply statistical methods that account for the variances (and possibly covariances) of such measures. The estimated parameters together with their estimated variances can be incorporated into a general linear mixed model framework. We illustrate the methodology by using data from a first-in-man study and a simulated data set. The analysis was implemented with the SAS procedure MIXED and example code is offered.
authors.affiliation: GlaxoSmithKline Biologicals, Rixensart, Belgium. fabian.tibaldi@skynet.be
authors.forename: Fabián
authors.initials: F
authors.lastname: Tibaldi
authors.forename: Didier
authors.initials: D
authors.lastname: Renard
authors.forename: Geert
authors.initials: G
authors.lastname: Molenberghs
mesh_terms: D002986:Clinical Trials as Topic / Q000592:standards / Q000706:statistics & numerical data*; D015331:Cohort Studies; D004305:Dose-Response Relationship, Drug; D006339:Heart Rate / Q000187:drug effects / Q000502:physiology; D006801:Humans; D007545:Isoproterenol / Q000494:pharmacology; D015201:Meta-Analysis as Topic
chemical_list: D007545:Isoproterenol