Statistical Methods for large-scale, prospective, epidemiologic studies

 描述(由申请者提供)：联邦政府在发展和维护大型预期队列方面的大量投资，如妇女健康倡议(WHI)，已经导致有关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。这些数据是阐明控制复杂疾病病因的因素的宝贵资源，这些疾病是由遗传、环境和生活方式因素的组合引起的。我们提出了统计方法，以更好地利用来自大规模、前瞻性流行病学调查的信息，如WHI。由于这样的研究招募了数十万名预期长期跟踪的受试者；在他们的设计中内置了几种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。尽管成本效益高，但自我报告很容易出错。通过适当地考虑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，非随机化环境下的因果推断以及挖掘高维数据集的方法。具体地说，我们的建议涉及以下具体目标：在容易出错的结果的背景下，我们提出以下具体目标：目标1：制定研究设计方法，纳入缺失数据的影响，并考虑具体的测试范式。目的2：扩展非随机化环境下的因果推理方法。目标3：开发高维数据环境中变量选择的方法。具体地说，我们提出了以下策略(3a)：针对分组特征的分级惩罚Cox模型；(3b)：基于非参数树的集成算法；(3c)：结合外部生物信息的贝叶斯变量选择方法。调查小组是跨学科的，有成功的合作记录，成员包括R.Balasubramanian博士(PI，加州大学阿默斯特分校生物统计学助理教授)，Y.Ma博士(共同研究员，加州大学马萨诸塞医学院医学副教授)，M.G.Tadesse博士(共同研究员，乔治敦大学统计学副教授)，R.A.Betensky博士(共同研究员，哈佛大学公共卫生学院生物统计学教授)，K.M.Rexrode博士(共同研究员，布里格姆和妇女医院医学副教授)和Ross L.Prentice博士(合作者，华盛顿大学生物统计学教授)。影响：大量的联邦投资已经提供了行为、基因和表型数据的巨大储存库，这些数据是从大型前瞻性研究中收集的，比如妇女健康倡议。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.