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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/2037

Title: Modeling anti-KLH ELISA data using two-stage and mixed effects models in support of immunotoxicological studies
Authors: Shkedy, Ziv
Straetemans, Roel
Molenberghs, Geert
Desmidt, Miek
Vinken, Petra
Goeminne, Nick
Coussement, Werner
Van den Poel, Bob
Bijnens, Luc
Issue Date: 2005
Publisher: TAYLOR & FRANCIS INC
Citation: JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 15(2). p. 205-223
Abstract: During preclinical drug development, the immune system is specifically evaluated after prolonged treatment with drug candidates, because the immune system may be an important target system. The response of antibodies against a T-cell-dependent antigen is recommenced by the FDA and EMEA for the evaluation of immunosuppression/ enhancement. For that reason, we developed a semiquantitative enzyme-linked immunosorbent assay to measure antibodies against keyhole limpet hemocyanin. To our knowledge, the analysis of this kind of data is at this moment not yet fully explored. In this article, we describe two approaches for modeling immunotoxic data using nonlinear models. The first is a two-stage model in which we fit an individual nonlinear model for each animal in the first stage, and the second stage consists of testing possible treatment effects using the individual maximum likelihood estimates obtained in the first stage. In the second approach, the inference about treatment effects is based on a nonlinear mixed model, which accounts for heterogeneity between animals. In both approaches, we use a three-parameter logistic model for the mean structure.
Notes: Limburgs Univ Ctr, Ctr Stat Biostat, B-3590 Diepenbeek, Belgium. Janssen Pharmaceut, Johnson & Johnson Pharmaceut Res & Dev, 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/1942/2037
DOI: 10.1081/BIP-200048815
ISI #: 000236232300003
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Validation: ecoom, 2007
Appears in Collections: Research publications

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