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 通常涉及协助实验设计或分析 数据的解释。此外，该项目还提出， 发展和完善统计方法和算法， 分析有毒物质测量数据，并建立新的和 改进测量技术。许多分析方法可以做得更多 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，因为它允许更多的 以同样的成本对危险场所进行频繁的监测， 协助开发和使用分析技术，以检测有毒物质 在临床上和现场的物质在较低的水平， 比现有方法更精确。特别强调的是 对改进的ELISA方案的发展的统计贡献 以及更好地分析来自现有协议的数据。技术 如经验贝叶斯估计、非线性最优设计、最大 伪似然，M估计将允许这些方法来处理 复杂的非线性校准问题，非恒定方差，可能 离群值和接近或低于检测限的值。

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.