Statistical Methods for large-scale, prospective, epidemiologic studies

 描述（由申请人提供）：联邦在开发和维护大型前瞻性队列（如妇女健康倡议（WHI））方面的重大投资，已导致数十万受试者的表型，行为和基因型信息的丰富数据库的可用性。这些数据是阐明复杂疾病病因学的宝贵资源，这些疾病是由遗传、环境和生活方式因素共同引起的。我们提出了统计方法，以更好地利用大规模的，前瞻性的流行病学调查，如WHI的信息。由于这些研究招募了数十万名受试者，这些受试者被前瞻性地长期随访;他们的设计中包含了一些具有成本效益的措施。这类调查的一个重要特点是，通过定期自我报告而不是通过直接测量来确定事件。虽然自我报告具有成本效益，但容易出错。通过适当地解释自我报告结果中的错误，我们专注于研究设计的统计工具的开发，非随机设置中的因果推理以及挖掘高维数据集的方法。具体而言，我们的建议解决了以下具体目标：在易出错的结果的背景下，我们提出了以下具体目标：目标1：开发研究设计方法，纳入缺失数据的影响，并考虑特定的测试范式。目标2：扩展非随机环境中因果推理的方法。目标3：开发高维数据环境中变量选择的方法。具体而言，我们提出了以下策略（3a）：分层惩罚考克斯模型的分组功能;（3b）：非参数，集成树为基础的算法;（3c）：贝叶斯变量选择方法，将外部生物信息。调查团队是跨学科的，有着成功合作的记录，包括R。Balasubramanian（PI，生物统计学助理教授，马萨诸塞大学阿默斯特分校），Y。马（合作研究者，医学副教授，麻省大学医学院），博士。G. Tadesse（联合研究员，乔治敦大学统计学副教授），R。A. Betensky（合作研究者，哈佛公共卫生学院生物统计学教授），K. M. Rexrode（共同研究者，医学副教授，布里格姆妇女医院）和博士罗斯L。普伦蒂斯（合作者，生物统计学教授，华盛顿大学）。影响：大量的联邦投资使得从大型前瞻性研究（如妇女健康倡议）中收集的行为、基因型和表型数据库变得可用。我们的跨学科团队建议开发和应用新的统计方法来有效地挖掘这些快速增长的数据库，以阐明糖尿病和心血管疾病等复杂疾病的病因。

 DESCRIPTION (provided by applicant): Significant federal investment in developing and maintaining large, prospective cohorts such as the Women's Health Initiative (WHI) has resulted in the availability of rich databases of phenotypic, behavioral and genotypic information on hundreds of thousands of subjects. These data are invaluable resources for elucidating the factors governing the etiology of complex diseases, which are caused by a combination of genetic, environmental, and lifestyle factors. We propose statistical methods to better leverage the information available from large-scale, prospective epidemiologic investigations such as the WHI. As such studies enroll several hundreds of thousands of subjects who are prospectively followed for long periods; several cost-effective measures are built in to their design. One significant feature of such investigations is that event ascertainment is through periodic self-reports rather than through direct measurement. Although cost-effective, self-reports are prone to error. By appropriately accounting for the error in self-reported outcomes, we focus on development of statistical tools for study design, causal inference in non-randomized settings as well as methods for mining high dimensional datasets. Specifically, our proposal addresses the following specifically aims: In the context of error-prone outcomes, we propose the following specifically aims: Aim 1: Develop methods for study design, incorporating the effects of missing data and considering specific testing paradigms. Aim 2: Extend methods for causal inference in non-randomized settings. Aim 3: Develop methods for variable selection in high dimensional data settings. Specifically, we propose the following strategies (3a): Hierarchically penalized Cox model for grouped features; (3b): Nonparametric, ensemble tree based algorithm; (3c): Bayesian variable selection methods incorporating external biological information. The investigative team is interdisciplinary with a track record of successful collaboration and include Dr. R. Balasubramanian (PI, Assistant Professor of Biostatistics, UMass-Amherst), Dr. Y. Ma (Co-investigator, Associate Professor of Medicine, UMass Medical School), Dr. M. G. Tadesse (Co-investigator, Associate Professor of Statistics, Georgetown University), Dr. R. A. Betensky (Co-investigator, Professor of Biostatistics, Harvard School of Public Health), Dr. K. M. Rexrode (Co-investigator, Associate Professor of Medicine, Brigham and Women's Hospital) and Dr. Ross L. Prentice (Collaborator, Professor of Biostatistics, University of Washington). IMPACT: Significant federal investment has made available huge repositories of behavioral, genotypic and phenotypic data collected from large, prospective studies such as the Women's Health Initiative. Our interdisciplinary team proposes to develop and apply new statistical methods to effectively mine these rapidly growing databases to elucidate the etiology of complex disorders such as diabetes and cardiovascular disease.