Data Adaptive Estimation in Genomics and Epidemiology

基因组学和流行病学中的数据自适应估计

• 批准号: 6807110
• 负责人: Mark J Vanderlaan
• 金额: $ 24.01万
• 依托单位: UNIVERSITY OF CALIFORNIA BERKELEY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-01 至 2007-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6807110
• 关键词: bioinformatics; clinical research; computer data analysis; computer program /software; epidemiology; functional /structural genomics; health science research analysis /evaluation; human data; method development; statistics /biometry

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.

描述(由申请人提供)：该项目的广泛目标是基于新的理论结果，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适应性估计方法。现有的估计器选择方法最近已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。建议的方法将充分利用数据中包含的信息，以提供对为基因组学和流行病学研究收集的数据进行统计分析时感兴趣的参数的最佳估计。这些方法将被开发用于关联分析、生存、分析、因果推断、转录因子结合位点检测、微阵列数据分析以及点处理和纵向数据。基于估计函数方法的复杂估计方法将与一般方法相结合，以提供复杂纵向数据的估计。提出的估计过程依赖于三个部分：统一的交叉验证估计器选择方法，特定于筛子的估计器的构造，以及生成感兴趣参数的相应候选特定于筛子的估计器的积极算法，以便彻底搜索所有可能的估计器的空间。研究和检验了一种构造离散筛估计量和数据自适应选择相应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取进行了比较。 最终，这个项目将开发开源的、计算密集型的统计包，供公共卫生的研究人员使用R和sPlus接口。这些软件包将为基因组学和流行病学中的问题提供一系列数据自适应估计器的黑盒实现。它们将包括用C编写的例程，以提高算法中计算密集型部分的计算速度，并将与主题专家一起开发，以确保足够满足公共卫生研究的需要。这些例程将应用于专题专家提供的公开数据或真实数据，从而能够立即测试拟议的方法和软件。此外，模拟真实数据研究将允许与当前使用的估计方法相比，真实地检查方法的性能。这些用于Windows、Linux和Mac OS平台的包的分发将使用R和Bio-Conductor项目。这些包将包括详细的文档、示例和数据集。