SEMIPARAMETRIC REGRESSION TECHNIQUES FOR THE ANALYSIS OF BIOMEDICAL DATA

用于生物医学数据分析的半参数回归技术

• 批准号: 6325818
• 负责人: Joan G. Staniswalis
• 金额: $ 4.04万
• 依托单位: UNIVERSITY OF TEXAS EL PASO
• 依托单位国家: 美国
• 财政年份: 2000
• 资助国家: 美国
• 起止时间: 2000-06-01 至 2001-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6325818
• 关键词: computer simulation; human data; longitudinal human study; mathematical model; method development; statistics /biometry

The long term goals of this research program are to develop statistical methodology for the analysis and biomedical data that is based on non- and semi-parametric curve estimation theory, and to develop tools for parametric model building. Computer simulation studies will be executed to verify that the asymptotic theory developed is useful for interference with sample sizes typically encountered in biomedical data sets described in this proposal. The following problems will be studied during the proposed funding period: 1. Estimation in the Additive Model for Non-parametric Regression: An algorithm is proposed here for fitting the additive model in multiple regression. Consistency of the estimators should hold without restricting dependencies among the co-variates. 2. Non-parametric Regression Analysis of Multi-variate Longitudinal Data. a. This research project is motivated by a data set that was obtained from Phase I study to determine the safety of Droloxifene in patients with advanced metastatic breast cancer. The levels of several hormones were monitored at the time of entry to the study and repeatedly thereafter. This is a continuation of research reported in Staniswalis and Lee (1997). The smooth non-parametric estimators of the covariance function will be studied further. Development of methods for a canonical correlation analysis for random curves is proposed to characterize associations among the four different hormones over time, and to determine if there is a dependence on the dose of Droloxifene. b. Redundancy analysis is used when it is of interest to predict one set of variables with a predictor constructed from another set of variables. For this specific aim, this standard multi-variate methodology of redundancy analysis for vectors will be extended to understand how a collection of random curves could be used to best predict another collection of random curves.

该研究计划的长期目标是开发基于非和半参数曲线估计理论的分析和生物医学数据的统计方法，并开发用于参数模型构建的工具。将执行计算机仿真研究以验证开发的渐近理论对于干扰本提案中所述的生物医学数据集通常遇到的样本量。将在拟议的资金期间研究以下问题：1。非参数回归的添加剂模型中的估计：此处提出了一种算法，以将添加剂模型拟合到多重回归中。估计器的一致性应在不限制共同变化之间的依赖性的情况下保持。 2。多变量纵向数据的非参数回归分析。一个。该研究项目是由从I期研究获得的数据集的动机，该数据集是确定高级转移性乳腺癌患者的二氧酮的安全性。在进行研究时，对几种激素的水平进行了监测，此后反复进行。这是Staniswalis和Lee（1997）中报道的研究的延续。将进一步研究协方差函数的平滑非参数估计器。提出了用于随机曲线的规范相关分析的方法，以表征四种不同激素随着时间的流逝，并确定是否依赖于droxifene剂量。 b。当感兴趣的是，使用另一组变量构建的预测变量，使用冗余分析。对于这个具体目的，将扩展对向量的冗余分析的标准多变量方法，以了解如何使用随机曲线的集合来最好地预测另一个随机曲线的集合。