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Evaluation of Effect Profiles: Functional Observational Battery Outcomes
Author(s) -
Sandra J. S. Baird,
Paul J. Catalano,
Louise Ryan,
John S. O. Evans
Publication year - 1997
Publication title -
toxicological sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.352
H-Index - 183
eISSN - 1096-6080
pISSN - 1096-0929
DOI - 10.1093/toxsci/40.1.37
Subject(s) - bivariate analysis , observational study , applicability domain , statistics , meta analysis , medicine , mathematics , computer science , quantitative structure–activity relationship , machine learning
The Functional Observational Battery (FOB) is a neurotoxicity screening assay composed of 25-30 descriptive, scalar, binary, and continuous endpoints. These outcomes have been grouped into six biologically logical domains as a means to interpret the neuroactive properties of tested chemicals (V. C. Moser, 1992, J. Am. Coll. Toxicol. 10(6), 661-669). However, no data-based exploration of these functional domains has been done. We investigated the degree to which experimental data correspond to the domain groupings by examining severity scores from 10 chemicals tested using a standardized protocol for acute exposure (V. C. Moser et al., 1995, J. Toxicol. Environ. Health 45, 173-210) and identifying endpoint groupings (factors) that best describe the interrelationships in the data, allowing a statistical assessment of whether the FOB endpoints break into domains. We also used a standard measure of bivariate association to confirm the results of the factor analysis. Our results show that while there are clear relationships among variables that compose some domains, there is often substantial correlation among endpoints in different domains. In addition, we investigated a related issue concerning the relative power of the chosen endpoint groupings for identifying significant domain effects. Results from a randomization analysis of the 10 chemicals suggest that the neurophysiologic domain structuring may provide some degree of statistical efficiency for identifying effects.

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