Statistical Methods for Medical Studies

描述（由申请人提供）：本次竞争性更新的目的是开发生物医学研究的统计方法，主要关注慢性疾病人群研究所需的技术和工具。有一个主要的重点是统计设计和分析程序，使用高维基因组和蛋白质组数据，即将可用。这些数据有很大的潜力，以刺激各种重要的研究领域，包括早期发现的疾病，疾病风险分析，预防性干预的发展，并阐明预防性干预的效果。还继续强调重要环境数据的建模和使用（例如，饮食）暴露;在几个重要的研究设计下避免偏倚;以及在慢性病人群研究议程中这些设计之间的相互作用。该计划将继续目前的三个项目和行政核心。项目1关注与流行病学队列研究和疾病预防试验相关的统计学主题。这些包括失败时间数据分析中的几个主题;饮食和体力活动测量误差调节的方法;全基因组单核苷酸多态性关联研究（GWAS）中边缘效应的设计和分析方法;以及队列研究和随机对照试验之间的差异偏倚研究。项目2主要关注GWAS边缘关联之外的分析。主题包括基于群体和基于家庭的关联研究数据的组合;基因-基因和基因-环境相互作用的识别和评估;以及在受影响同胞对的多点分析中连锁和单体型关联的同时估计。项目3涉及用于疾病早期检测和其他目的的生物标志物发现和评估方法。主题包括基于功能数据（例如，质谱），例如，在蛋白质组学研究中出现的;评估疾病生物标志物的预测价值的方法的开发;以及生物标志物选择和验证的顺序设计的研究。总的来说，这些项目将应用14个致力于和互动的统计和数学科学家的人才，以解决统计问题，这些问题是慢性病人群研究进展中最重要的。

DESCRIPTION (provided by applicant): The aim of this competing renewal is the development of statistical methods for biomedical research, with principal focus on techniques and tools needed for chronic disease population research. There is a major emphasis on statistical design and analysis procedures for using the high-dimensional genomic and proteomic data that are coming available. These data have much potential to stimulate various important research areas including early detection of disease, disease risk profiling, preventive intervention development, and the elucidation of preventive intervention effects. There is also a continuing emphasis on the modeling and use of data on important environmental (e.g., dietary) exposures; on the avoidance of bias under several important study designs; and on the interplay among such designs in the chronic disease population research agenda. The Program will continue the current three projects and administrative core. Project 1 is concerned with statistical topics relevant to epidemiologic cohort studies and disease prevention trials. These include several topics in the analysis of failure time data; methods for dietary and physical activity measurement error accommodation; design and analysis methods for marginal effects in genome-wide single nucleotide polymorphism association studies (GWAS); and study of differential biases between cohort studies and randomized controlled trials. Project 2 focuses primarily on analyses beyond marginal associations for GWAS. Topics include combination of data from population-based and family-based association studies; the identification and assessment of gene-gene and gene-environment interactions: and the simultaneous estimation of linkage and haplotype associations in multipoint analysis of affected sib-pairs. Project 3 is concerned with biomarker discovery and evaluation methods for early detection of disease and for other purposes. Topics include design and analysis methods to distinguish cases from controls based on functional data (e.g., mass spectra) as arise, for example, in proteomic research; the development of methods to assess the predictive value of disease biomarkers: and the study of sequential designs for biomarker selection and validation. Collectively, these projects will apply the talents of 14 committed and interactive statistical and mathematical scientists to address statistical topics that are among the most important for progress in chronic disease population research.