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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2038

Title: A hierarchical binomial-poisson model for the analysis of a crossover design for correlated binary data when the number of trials is dose-dependent
Authors: Shkedy, Ziv
Molenberghs, Geert
Van Craenendonck, Hansfried
Bijnens, Luc
Steckler, Thomas
Issue Date: 2005
Publisher: TAYLOR & FRANCIS INC
Citation: JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 15(2). p. 225-239
Abstract: The differential reinforcement of a low-rate 72-seconds schedule (DRL-72) is a standard behavioral test procedure for screening a potential antidepressant compound. The data analyzed in the article are binary outcomes from a crossover design for such an experiment. Recently, Shkedy et al. ( 2004) proposed to estimate the treatments effect using either generalized linear mixed models (GLMM) or generalized estimating equations ( GEE) for clustered binary data. The models proposed by Shkedy et al. ( 2004) assumed the number of responses at each binomial observation is fixed. This might be an unrealistic assumption for a behavioral experiment such as the DRL-72 because the number of responses ( the number of trials in each binomial observation) is expected to be influenced by the administered dose level. In this article, we extend the model proposed by Shkedy et al. ( 2004) and propose a hierarchical Bayesian binomial-Poisson model, which assumes the number of responses to be a Poisson random variable. The results obtained from the GLMM and the binomial-Poisson models are comparable. However, the latter model allows estimating the correlation between the number of successes and number of trials.
Notes: Limburgs Univ Ctr, Ctr Stat Biostat, B-3590 Diepenbeek, Belgium. Janssen Pharmaceut, Johnson & Johnson Pharmaceut Res & Dev, B-2340 Beerse, Belgium.Shkedy, Z, Limburgs Univ Ctr, Ctr Stat Biostat, Univ Campus, B-3590 Diepenbeek, Belgium.ziv.shkedy@luc.ac.be
URI: http://hdl.handle.net/1942/2038
DOI: 10.1081/BIP-200049825
ISI #: 000236232300004
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Validation: ecoom, 2007
Appears in Collections: Research publications

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