CORE--STATISTICAL ANALYSIS OF TOXIC MEASUREMENT DATA

这个项目有两个目的。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。这将是 通常涉及实验设计或分析方面的协助 和数据的解释。此外，该项目还提出了 统计方法和算法的发展和改进 分析有毒物质测量数据并创建新的和 改进了测量技术。许多分析方法可以做得更好 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 以同样的成本频繁监测危险地点，而且由于 艾滋病在开发和使用检测有毒物质的分析技术方面 临床和现场低水平的物质，以及 比现有方法更准确。特别强调的是 对改进的酶联免疫吸附试验方案发展的统计贡献 以及对现有协议的数据进行更好的分析。技术 如经验贝叶斯估计、非线性最优设计、最大值 伪似然估计和M估计将允许这些方法处理 复杂的非线性校准问题，非常数方差，可能 异常值，以及接近或低于检测下限的值。

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.