Data Adaptive Estimation in Genomics and Epidemiology

项目描述（由申请人提供）：本项目的主要目标是基于新的理论成果，为基因组学和流行病学研究开发、研究、测试和实施新的数据自适应估计方法。现有的估计器选择程序最近被证明低估了有限样本数据中用于选择感兴趣参数的适当估计器的信息量。根据定义，这些参数用于直接回答感兴趣的公共卫生问题。所提出的方法将充分利用数据中包含的信息，为基因组学和流行病学研究收集的数据的统计分析提供感兴趣的参数的最佳估计。这些方法将用于关联分析、生存分析、因果推断、转录因子结合位点检测、有或没有审查数据的微阵列数据分析，以及点治疗和纵向数据。基于估计函数方法的复杂估计方法将与考虑为复杂纵向数据提供估计的一般方法相结合。提出的估计过程依赖于三个组成部分：统一的交叉验证估计器选择方法，特定于筛的估计器的构建，以及生成感兴趣参数的相应候选特定于筛的估计器的积极算法，以便彻底搜索所有可能估计器的空间。研究了一种构造离散筛估计量和数据自适应选择最佳估计量的新方法，并与常用的连续筛估计量的构造和选择进行了比较。

DESCRIPTION (provided by applicant): The broad objective of this project is to develop, study, test, and implement new data adaptive estimation methods for research in Genomics and Epidemiology based on new theoretical results. Existing estimator selection procedures have recently been proved to underestimate the amount of information in finite sample data used for selecting an appropriate estimator of a parameter of interest. Such parameters are by definition used to directly answer Public Health questions of interest. The methods proposed will fully exploit the information contained in data to provide the best estimates of parameters of interest in statistical analyses of data collected for research in Genomics and Epidemiology. These methods will be developed for association analysis, survival, analysis, causal inference, transcription factor binding site detection, microarray data analysis with or without censored data, and for point treatment and longitudinal data. Complex estimation methodologies based on estimating function approaches will be combined to the general methodology considered to provide estimates for complex longitudinal data. The estimation procedure proposed relies on three components: a unified cross-validation estimator selection methodology, construction of sieve-specific estimators, and an aggressive algorithm for generating the corresponding candidate sieve-specific estimators of a parameter of interest so as to thoroughly search the space of all possible estimators. A new method for constructing discrete sieve estimators and data adaptively selecting the corresponding best estimator will be studied and tested in comparison with the construction and selection of common continuous sieve estimators. Ultimately this project will develop open source, computationally intensive, statistical packages for use with the R and Splus interfaces by researchers in Public Health. These packages will provide black box implementations of a range of data adaptive estimators for problems in Genomics and Epidemiology. They will include routines written in C to enhance the computation speed of the portions of the algorithms that are computationally intensive and will be developed with subject-matter experts to ensure adequacy for the needs of Public Health research. The routines will be applied on publicly available data or real data provided by the subject-matter experts, enabling immediate testing of the proposed methods and software. In addition, simulations imitating real data studies will allow a truthful check of the performance of the methods in comparison to the current estimation methods used. Distribution of these packages for Windows, Linux and Mac OS platforms will use the R and Bio-conductor projects. These packages will include detailed documentation, examples, and data sets.