Estimating reliability and generalizability from hierarchical biomedical data.

Full 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 exemplified using clinical studies in schizophrenia, with focus on the endpoints Clinician's Global Impression (CGI) or Positive and Negative Syndrome Scale (PANSS).

Author information

Author/s: Molenberghs, Geert (G); Laenen, Annouschka (A); Vangeneugden, Tony (T);

Affiliation: Center for Statistics, Hasselt University, Diepenbeek, Belgium. geert.molenberghs(-atsign-)uhasselt.be

Journal and publication information

Publication Type: Journal Article; Research Support, Non-U.S. Gov't

Journal: Journal of biopharmaceutical statistics (J Biopharm Stat), published in United States. (Language: eng)

Reference: 2007-; vol 17 (issue 4) : pp 595-627

Dates: Created 2007/07/06; Completed 2007/09/04; Revised 2007/11/15;

PMID: 17613644, status: MEDLINE (last retrieval date: 2/18/2009, IMS Date: )

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