CORE--STATISTICAL ANALYSIS OF TOXIC MEASUREMENT DATA

核心--毒性测量数据统计分析

• 批准号: 6217609
• 负责人: David M. Rocke
• 金额: $ 16.36万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-04-01 至 2000-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6217609
• 关键词: analytical method; biomedical facility; computer data analysis; computer program /software; environmental toxicology; enzyme linked immunosorbent assay; experimental designs; hazardous substances; mathematical model; method development; statistics /biometry

This project has two purposes. As the statistics core, the project will provide statistical support to other Superfund projects. This will typically involve assistance in experimental design or in the analysis and interpretation of data. In addition, this project proposes the development and refinement of statistical methods and algorithms for the analysis of toxics measurement data and for the creation of new and improved measurement techniques. Many analytical methods can be made more efficient and effective by careful statistical design and analysis of the data. This may be important to human health since its allows more frequent monitoring of hazardous sites for the same cost, and since it aids in the development and use of analytical techniques to detect toxic substances both clinically and in the field at lower levels and with greater accuracy than existing methods. A particular emphasis is on statistical contributions to the development of improved ELISA protocols and to the better analysis of data from existing protocols. Techniques such as empirical Bayes estimation, nonlinear optimal design, maximum pseudo likelihood, and M estimation will allow these methods to deal with complex problems of nonlinear calibration, nonconstant variance, possible outliers, and values near or below detection limits.

该项目有两个目的。作为统计核心，该项目将 为其他超级基金项目提供统计支持。这会 通常涉及实验设计或分析 和数据解释。此外，该项目提出了 统计方法和算法的开发和完善 分析有毒物质测量数据以及创建新的和 改进的测量技术。可以使许多分析方法更多 通过仔细的统计设计和分析 数据。这可能对人类健康很重要，因为它允许更多 经常以相同的费用对危险站点进行监控，既然 有助于开发和使用分析技术来检测有毒 临床和现场的物质在较低的水平以及 比现有方法更高的精度。特别强调 对改进ELISA协议的开发的统计贡献 并更好地分析现有协议的数据。技术 例如经验贝叶斯估计，非线性最佳设计，最大 伪的可能性和m估计将允许这些方法处理 非线性校准，非稳定方差的复杂问题，可能 离群值以及接近或低于检测限的值。