Statistical Informatics for Cancer Research

本续期申请建议开展统计方法研究项目，以解决在癌症研究中观察性研究的大型复杂数据分析中出现的差距和障碍。该项目的最终目标是利用丰富的数据来源制定有效的策略，以减少美国的癌症负担，提高寿命和生活质量。本项目包括三个研究项目和两个核心项目。这三个综合项目共同满足国家癌症研究所癌症控制和人口科学司确定的三个研究优先领域的统计需要：健康差异；比较有效性研究；以及公共健康基因组学。在项目1中，我们将开发统计方法来克服跨越癌症连续体的社会和种族差异调查的常见数据限制。我们将分析来自SEER数据库的数据，该数据库与国家纵向死亡率调查（NLMS）的数据相关联。在项目2中，我们将利用大量观测数据开发癌症比较有效性研究（CER）的方法。我们将使用SEER-Medicare数据和CaPSURE队列来模拟复杂的随机试验，以比较个性化癌症诊断策略和动态癌症治疗策略的有效性。在Project 3中，我们将开发用于遗传癌症流行病学研究中下一代测序数据分析的统计方法。项目3拟开展的研究受到哈佛肺癌和乳腺癌外显子组和靶向测序研究以及相关的全基因组关联研究的启发和应用。行政核心将协调该计划的整体科学方向和计划活动，包括定期P01会议、研讨会、年度务虚会、外部咨询委员会会议、短期课程、访问计划、研究成果的传播。统计计算核心将允许访问哈佛最大的高性能计算集群，执行数据管理，并确保开发和传播开放访问，高质量的软件。项目pi林西红教授和Francesca Dominici教授是著名的生物统计学家，在方法论和合作研究以及学术管理方面有着良好的记录。

This renewal application proposes to carry out a Program Project of statistical methods research to address gaps and barriers arising in the analysis of large and complex data from observational studies in cancer research. The ultimate goal of the Program is to use rich data sources to develop effective strategies for reducing cancer burden in the U.S. and improving longevity and quality of life. This Program Project comprises three research projects and two cores. The three integrated projects jointly address the statistical needs for three research priority areas identified by the Division of Cancer Contro and Population Science of National Cancer Institute: Health Disparities; Comparative Effectiveness Research; and Public Health Genomics. In Project 1, we will develop statistical methods to overcome common data limitations for the investigation of social and racial disparities spanning the cancer continuum. We will analyze data from the SEER database that is linked with data from the National Longitudinal Mortality Survey (NLMS). In Project 2, we will develop methods for comparative effectiveness research (CER) in cancer using large observational data. We will use the SEER-Medicare data and the CaPSURE cohort to emulate complex randomized trials to compare the effectiveness of personalized strategies for cancer diagnosis and dynamic strategies for cancer treatment. In Project 3, we will develop statistical methods for analysis of next generation sequencing data in genetic cancer epidemiological studies. The proposed research in Project 3 is motivated by and applied to the Harvard lung cancer and breast cancer exome and targeted sequencing studies as well as the affiliated Genome-Wide Association Studies. The Administrative Core will coordinate the overall scientific direction and programmatic activities of the Program, which will include regular P01 meetings, seminars, the annual retreat, the external advisory committee meeting, short courses, a visitor program, dissemination of research results. The Statistical Computing Core will allow access to Harvard largest high performance computing cluster, perform data management, and ensure the development and dissemination of open access, high quality software. The Program PIs, Professors Xihong Lin and Francesca Dominici, are renowned biostatisticians with strong track records of methodological and collaborative research and academic administration.