Statistical Informatics for Cancer Research

癌症研究统计信息学

基本信息

批准号：
9098450
负责人：
Francesca Dominici
金额：
$ 69.33万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
依托单位国家：
美国
项目类别：
财政年份：
2008
资助国家：
美国
起止时间：
2008-09-10 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9098450
关键词：
Address Advisory Committees Applied Research Area Cancer Burden Cancer Etiology Cancer Patient Clinical Communities Complex Computer software Data Data Analyses Data Sources Databases Development Diagnostic Ensure Epidemiologic Studies Epidemiology Faculty Family Study Genomics Goals High Performance Computing Incidence Individual Informatics Investigation Knowledge Link Longevity Malignant Neoplasms Malignant neoplasm of lung Malignant neoplasm of prostate Mediating Mediation Medical center Medicare Methods Mission National Cancer Institute Observational Study Population Population Sciences Postdoctoral Fellow Predisposition Prevention Preventive Intervention Public Health Quality of life Records Research Research Priority Research Project Grants Risk Assessment Social Behavior Socioeconomic Status Statistical Computing Statistical Methods Structural Models Students Surveys Time Uncertainty Work anticancer research base breast cancer survival cancer diagnosis cancer genetics cancer health disparity cancer risk cancer survival cancer therapy cohort comparative effectiveness compare effectiveness data management dissemination research drug development effectiveness research exome exome sequencing genetic profiling genetic variant genome wide association study health disparity improved innovation malignant breast neoplasm meetings mortality next generation sequencing novel therapeutics outcome forecast personalized care personalized medicine personalized strategies population based predictive modeling professor program dissemination programs racial and ethnic disparities racial disparity randomized trial social social disparities socioeconomics targeted sequencing tool treatment strategy user friendly software

项目摘要

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.

该更新应用建议在癌症研究中观察性研究的大型和复杂数据时，进行统计方法研究的计划项目，以解决差距和障碍。该计划的最终目标是使用丰富的数据源来制定有效的策略来减轻美国的癌症负担并改善寿命和生活质量。该计划项目包括三个研究项目和两个核心。这三个综合项目共同解决了国家癌症研究所的癌症侵害和人口科学划分的三个研究优先领域的统计需求：健康差异；比较有效性研究；和公共卫生基因组学。在项目1中，我们将开发统计方法，以克服跨越癌症连续体的社会和种族差异的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，我们将使用大型观察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队列模拟复杂的随机试验，以比较个性化策略在癌症诊断和癌症治疗的动态策略的有效性。在项目3中，我们将开发统计方法，用于分析遗传癌流行病学研究中的下一代测序数据。项目3中的拟议研究是由哈佛肺癌和乳腺癌外显子和靶向测序研究以及关联整个基因组关联研究的动机并应用的。行政核心将协调该计划的整体科学方向和程序活动，其中包括定期的P01会议，研讨会，年度务虚会，外部咨询委员会会议，短期课程，访客计划，访问研究结果的传播。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，并确保开放访问，高质量软件的开发和传播。 PIS计划，Xihong Lin教授和Francesca Dominici，是著名的生物统计学家，具有强大的方法论和协作研究和学术管理记录。