Evaluation of effect profiles: Functional Observational Battery outcomes.
Evaluation of effect profiles: Functional Observational Battery outcomes.
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效果概况评估：功能观察电池结果。
DOI：
10.1006/faat.1997.2357
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发表时间：
1997
期刊：
影响因子：
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通讯作者：
Evans,JS
中科院分区：
文献类型：
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作者：
Baird,SJ;Catalano,PJ;Ryan,LM;Evans,JS
功能观察电池 (FOB) 是一种神经毒性筛选试验，由 25-30 个描述性、标量、二元和连续终点组成。这些结果被分为六个生物学逻辑领域，作为解释测试化学物质的神经活性特性的一种手段（V. C. Moser, 1992, J. Am. Goll. Toxicol. 10(6), 661–669）。然而，尚未对这些功能域进行基于数据的探索。我们通过检查使用急性暴露标准化方案测试的 10 种化学品的严重性评分（V. C. Moseret al., 1995, J. Toxicol Environ. Health 45, 173–210），并确定最能描述数据中相互关系的端点分组（因素），从而研究实验数据与域分组的对应程度，从而可以对 FOB 端点是否闯入域进行统计评估。我们还使用双变量关联的标准测量来确认因子分析的结果。我们的结果表明，虽然构成某些域的变量之间存在明显的关系，但不同域中的端点之间通常存在很大的相关性。此外，我们还调查了一个相关问题，涉及所选端点分组的相对功效，以识别显着的域效应。对 10 种化学物质的随机分析结果表明，神经生理学领域的结构可以为识别效应提供一定程度的统计效率。
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. Goll. 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. Moseret 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.