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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/9179

Title: Accounting for variability in individual hierarchical clinical trial data
Authors: Tibaldi, Fabian
Renard, Didier
Molenberghs, Geert
Issue Date: 2008
Publisher: JOHN WILEY & SONS INC
Citation: PHARMACEUTICAL STATISTICS, 7(4). p. 285-293
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. Copyright (C) 2007 John Wiley & Sons, Ltd.
Notes: [Tibaldi, Fabidn] GlaxoSmithKline Biol, B-1330 Rixensart, Belgium. [Renard, Didier] Novartis, Basel, Switzerland. [Molenberghs, Geert] Hasselt Univ, Ctr Stat, Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/9179
DOI: 10.1002/pst.313
ISI #: 000261910900007
ISSN: 1539-1604
Category: A1
Type: Journal Contribution
Validation: ecoom, 2010
Appears in Collections: Research publications

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