Conference on "Statistical Methods for Complex Biomedical Data"

描述（由申请人提供）：定量方法在设计和分析基础科学、癌症研究中的观察性和随机研究以及更普遍的健康科学研究中变得越来越重要。现代生物医学研究已经变得更加跨学科。随着生物技术的快速发展和大量生物医学数据，特别是基因组学和蛋白质组学数据的日益复杂，出现了大量具有挑战性的统计和计算问题。传统的统计技术不能满足这些新的需求，导致越来越需要开发创新的统计和计算方法，以解决在分析大型和复杂的生物医学数据集时出现的这些问题。因此，如果有一个平台，生物统计学家和科学家可以在其中批评现有的方法，提出和讨论已经开发的创新的新方法，并确定未来研究的优先事项，这将是非常有价值的。为了满足这一需求，我们提议召开一次“复杂生物医学数据的统计方法”会议，该会议将涵盖对癌症研究和一般生物医学研究至关重要的主题，包括营养流行病学、测量误差模型、非参数和半参数推断、功能数据分析、计算生物学和遗传流行病学。这次会议将为定量和生物医学研究人员提供一个促进跨学科互动的场所。会议将在德克萨斯农工大学举行，该大学拥有一个领先的统计学系。为了增加其宣传和允许最大的出席，它将被安排在2009年在德克萨斯州圣安东尼奥举行的国际生物识别学会北美东部地区会议之前举行。具体目标是“提供一个及时和刺激的平台，让生物统计学家、定量研究人员和生物医学科学家参与对新兴统计和计算问题的前沿深入讨论，特别是在计算生物学和遗传流行病学、营养流行病学、测量误差、非参数和半参数回归方法以及分析功能数据的方法等领域。”“促进和促进跨学科研究，并将讨论和结果传播给定量和主题生物医学研究人员，并让初级研究人员参与这些问题。

DESCRIPTION (provided by applicant): Quantitative methods have become increasingly important in the design and analysis of basic science, observational and randomized studies in cancer research, and health sciences research more generally. Modern biomedical research has become much more interdisciplinary. With the rapid advance of biotechnology and growing complexity of massive biomedical data, especially in genomics and proteomics, a significant number of challenging statistical and computational issues have emerged. Traditional statistical techniques cannot meet these new demands, leading to an increasing need to develop innovative statistical and computational methods to address these emerging issues in analyzing large and complex biomedical data sets. It would therefore be very valuable to have a platform in which biostatisticians and scientists can critique existing methods, present and discuss innovative new methods that have been developed, and identify priorities for future research. To respond to such a need, we propose a conference "Statistical Methods for Complex Biomedical Data", which would cover topics crucial to cancer research, and biomedical research in general, including nutritional epidemiology, measurement error models, nonparametric and semiparametric inference, functional data analysis, computational biology, and genetic epidemiology. This conference would provide quantitative and biomedical researchers a venue for stimulating interdisciplinary interaction. The Conference will be held at Texas A&M University in College Station, Texas, which houses a leading Statistics department. To increase its publicity and allow maximal attendance, it will be scheduled to take place just before the East North American Region meetings of the International Biometric Society which are in San Antonio, Texas in 2009. The specific aims are "To provide a timely and stimulating platform to engage biostatisticians, quantitative researchers, and biomedical scientists in cutting-edge in-depth discussions of emerging statistical and computational issues, especially in the areas of computational biology and genetic epidemiology, nutritional epidemiology, measurement error, nonparametric and semiparametric regression methods, and methods to analyze functional data. "To promote and foster interdisciplinary research and disseminate the discussions and the results to quantitative and subject-matter biomedical researchers, and engage junior investigators in these issues. Public Health Relevance: With the rapid advance of biotechnology and the increasing complexity of the data generated by biomedical studies, there are growing quantitative challenges that arise in cancer research, and biomedical research in general, that must be successfully addressed in order to make progress. Biostatistical researchers are working to address these challenges by developing and disseminating new statistical methodology that is needed in these fields. In this grant, we propose a conference "Statistical Methods for Complex Biomedical Data", in which we organize some of the top researchers in biostatistics to come together and present important cutting edge quantitative work in several key areas that are timely important for cancer research, and providing a platform for senior and junior investigators, quantitative and subject-matter biomedical researchers to engage in these issues.