评价效果概况:功能性观察电池结果。

S. Baird, Paul J. Catalano, Louise M. Ryan, John S. Evans
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引用次数: 9

摘要

功能观察电池(FOB)是一种由25-30个描述性、标量、二元和连续终点组成的神经毒性筛选试验。这些结果被分为六个生物学领域,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。科尔。中华毒物学杂志,10(6),661-669。然而,还没有对这些功能领域进行基于数据的探索。我们通过使用标准化的急性暴露方案检查10种化学物质的严重程度评分,调查了实验数据与域分组对应的程度(V. C. Moser et ., 1995, J. Toxicol)。环绕。健康45,173-210),并确定最能描述数据中相互关系的端点分组(因素),从而可以对FOB端点是否进入域进行统计评估。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。我们的研究结果表明,虽然组成某些域的变量之间存在明确的关系,但不同域的端点之间往往存在实质性的相关性。此外,我们还研究了一个相关的问题,即所选择的终点分组在识别显著域效应方面的相对能力。对这10种化学物质的随机分析结果表明,神经生理结构域的结构可能在一定程度上为识别效果提供了统计效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of effect profiles: Functional Observational Battery outcomes.
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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