Statistical Informatics for Cancer Research

癌症研究统计信息学

• 批准号: 8323844
• 负责人: XIHONG LIN
• 金额: $ 61.46万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-09-10 至 2013-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8323844
• 关键词: Accounting; Achievement; Address; Advisory Committees; American; American Cancer Society; Area; Behavioral; Bioinformatics; Biological Markers; Biological Markers; Biology; Biomedical Research; Biostatistical Methods; Biotechnology; Boston; Case-Control Studies; Cause of Death; Cessation of life; Childhood Leukemia; Clinical Trials; Communities; Complex; Computational Biology; Computing Methodologies; Coupled; Data; Data Set; Databases; Detection; Development; Diagnosis; Disease; Disease Clusterings; Disease model; Emerging Technologies; Enrollment; Ensure; Epidemiologic Methods; Epidemiologic Studies; Etiology; Explosion; Face; Foundations; Funding; Gender; Generations; Genes; Genetic; Genetic Polymorphism; Genomics; Geographic Information Systems; Goals; Government Agencies; Health; Health Insurance; Healthcare; Heart Diseases; Hospitals; Incidence; Individual; Informatics; Information Technology; Life Expectancy; Link; Malignant Neoplasms; Measures; Medical Surveillance; Methods; Modeling; Molecular Biology; Monitor; National Cancer Institute; Nature; Observational Study; Outcomes Research; Patients; Pattern; Pharmacologic Substance; Policies; Population Surveillance; Predisposition; Premature Mortality; Prevention strategy; Proportional Hazards Models; Proteomics; Publications; Quality of life; Race; Recording of previous events; Records; Registries; Reliance; Reporting; Research; Research Design; Research Personnel; Research Project Grants; Risk; Risk Assessment; Risk Factors; Scientific Advances and Accomplishments; Screening for cancer; Screening procedure; Series; Software Design; Source; Statistical Computing; Statistical Methods; Study Subject; Survival Analysis; Techniques; Thinking; Time; anticancer research; base; cancer therapy; data mining; design; disease natural history; disorder risk; dissemination research; effective therapy; falls; health disparity; indexing; innovation; malignant breast neoplasm; markov model; mortality; neoplasm registry; population based; professor; programs; response; social; socioeconomics; sound; surveillance data; user friendly software

DESCRIPTION (provided by applicant): We propose a Program Project, Statistical Informatics in Cancer Research, to tackle a series of problems motivated by the analysis of high dimensional data arising in population-based studies of cancer. This Program Project comprises three research projects and two cores. Project 1 focuses on spatio-temporal modeling of disease count data collected for administrative areas. The specific aims are motivated by problems encountered in epidemiological studies designed to monitor and assess health disparities. Our proposed methods address issues associated with administrative boundaries changing over time, sparse disease counts, spatial confounding, and heavy computational burdens for large data sets. Methods will be applied to data on U.S. breast cancer incidence from three state cancer registries, Boston-area premature mortality, and NCI SEER data. Project 2 is also motivated by spatially-indexed data related to cancer incidence and mortality, but the emphasis is on population surveillance and spatial cluster detection. Three of the specific aims of Project 2 are motivated by the analysis of NCI SEER data and one from a case/control study designed to assess spatial clustering in childhood leukemia. This dataset also includes individual level data on several genetic biomarkers of susceptibility. One sub-aim of this project assesses gene-space interaction by studying whether disease clustering patterns differ according to genetic polymorphisms. Project 3 focuses on methods for the analysis of very high dimensional genomic and proteomic biomarkers. Extensions to spatially indexed genomic data are also considered in Project 3. All of the aims of the three projects are closely integrated with the motivating real world cancer studies in which the investigators are involved. The three projects link thematically through a focus on population-based, observational studies in cancer, as well as technically through the consideration of high-dimensional correlated data (arising from different sources) that require advanced statistical and computing methods. Several specific techniques (e.g. spatio-temporal modeling, penalized likelihoods, False Discovery Rates, hidden Markov models) are shared between two and in some cases all three projects. The two cores consist of an Administrative Core and a Statistical Computing Core. The Administrative Core will coordinate the overall scientific direction and programmatic activities of Program, which will include short courses, a visitor program, dissemination of research results, and an external advisory committee. A Statistical Computing Core will ensure the development and dissemination of open access, good quality, user friendly software designed to implement the statistical methods developed in the Research Projects, which is the final Specific Aim of each of the three projects. The Program Director and Co-Director, Professors Louise Ryan and Xihong Lin, respectively, are internationally known biostatisticians with strong track records of academic administration.

描述（由申请人提供）：我们提出了一个名为“癌症研究中的统计信息学”的计划项目，旨在解决基于人群的癌症研究中出现的高维数据分析引发的一系列问题。该计划项目包括三个研究项目和两个核心项目。项目 1 重点关注为行政区域收集的疾病计数数据的时空建模。这些具体目标是出于旨在监测和评估健康差异的流行病学研究中遇到的问题而制定的。我们提出的方法解决了与随时间变化的行政边界、稀疏疾病计数、空间混杂以及大数据集的繁重计算负担相关的问题。方法将应用于来自三个州癌症登记处的美国乳腺癌发病率数据、波士顿地区过早死亡率和 NCI SEER 数据。项目 2 的动机也是与癌症发病率和死亡率相关的空间索引数据，但重点是人口监测和空间聚类检测。项目 2 的三个具体目标是由 NCI SEER 数据分析推动的，其中一个目标来自旨在评估儿童白血病空间聚类的病例/对照研究。该数据集还包括几种易感性遗传生物标志物的个体水平数据。该项目的一个子目标是通过研究疾病聚类模式是否根据遗传多态性而有所不同，从而评估基因空间相互作用。项目 3 侧重于分析极高维基因组和蛋白质组生物标志物的方法。项目 3 还考虑了空间索引基因组数据的扩展。这三个项目的所有目标都与研究人员参与的现实世界癌症研究紧密结合。这三个项目通过关注基于人群的癌症观察性研究在主题上相互联系，在技术上通过考虑需要先进统计和计算方法的高维相关数据（来自不同来源）。几个特定技术（例如时空建模、惩罚似然、错误发现率、隐马尔可夫模型）在两个项目之间共享，在某些情况下是所有三个项目之间共享。这两个核心由管理核心和统计计算核心组成。行政核心将协调该计划的总体科学方向和计划活动，其中包括短期课程、访客计划、研究成果传播和外部咨询委员会。统计计算核心将确保开发和传播开放获取、优质、用户友好的软件，旨在实施研究项目中开发的统计方法，这是三个项目中每个项目的最终具体目标。项目主任和联合主任 Louise Ryan 教授和林熙红教授分别是国际知名的生物统计学家，拥有良好的学术管理记录。