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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/4023

Title: Estimating reliability and generalizability from hierarchical biomedical data
Authors: Molenberghs, Geert
Laenen, Annouschka
Vangeneugden, Tony
Issue Date: 2007
Publisher: TAYLOR & FRANCIS INC
Citation: JOURNAL OF BIOPHARMACEUTICAL STATISTICS, 17(4). p. 595-627
Abstract: It is shown how hierarchical biomedical data, such as coming from longitudinal clinical trials, meta-analyses, or a combination of both, can be used to provide evidence for quantitative strength of reliability, agreement, generalizability, and related measures that derive from association concepts. When responses are of a continuous, Gaussian type, the linear mixed model is shown to be a versatile framework. At the same time, the framework is embedded in the generalized linear mixed models, such that non-Gaussian, e.g., binary, outcomes can be studied as well. Similarities and, above all, important differences are studied. All developments are exempli. ed using clinical studies in schizophrenia, with focus on the endpoints Clinician's Global Impression (CGI) or Positive and Negative Syndrome Scale (PANSS).
Notes: Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium. Tibottec, Mechelen, Belgium.MOLENBERGHS, G, Hasselt Univ, Ctr Stat, Agoralaan 1, B-3590 Diepenbeek, Belgium.geert.molenberghs@uhasselt.be
URI: http://hdl.handle.net/1942/4023
DOI: 10.1080/10543400701329448
ISI #: 000248315300005
ISSN: 1054-3406
Category: A1
Type: Journal Contribution
Validation: ecoom, 2008
Appears in Collections: Research publications

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