Data Analysis and Management

数据分析与管理

• 批准号: 8565878
• 负责人: LYNNE R WILKENS
• 金额: $ 25.13万
• 依托单位: UNIVERSITY OF HAWAII AT MANOA
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8565878
• 关键词: Abdomen; Address; African American; Behavior; Biochemical; Biological; Biological Markers; Blood; Body fat; California; Clinical; Cohort Studies; Collaborations; Collection; Colorectal; Colorectal Cancer; Complex; Cross-Sectional Studies; DNA; Data; Data Analyses; Diagnosis; Dietary intake; Dimensions; Dual-Energy X-Ray Absorptiometry; Eating Behavior; Ensure; Ethnic group; Feces; Genetic Variation; Genome; Genomics; Goals; Hawaii; Hawaiian population; Heterogeneity; Hormonal; Image; Imaging Techniques; Incidence; Individual; Informatics; Information Systems; Intervention; Japanese Population; Knowledge; Laboratories; Latino; Life Style; Link; Magnetic Resonance Imaging; Malignant Neoplasms; Manuscripts; Measurement; Measures; Modeling; Monitor; Nested Case-Control Study; Nutritional Biochemistry; Obesity; Obesity associated cancer; Participant; Pathway interactions; Pattern; Phenotype; Physical activity; Population; Postmenopause; Preparation; Primary Neoplasm; Procedures; Process; Proxy; Recruitment Activity; Research; Research Design; Research Personnel; Research Project Grants; Risk; Sampling; Science; Software Tools; Stratification; Study Subject; Surveys; System; Testing; Visit; Woman; Work; base; cancer risk; cohort; data management; design; ethnic difference; experience; high risk; lifestyle factors; malignant breast neoplasm; meetings; member; men; metabolomics; microbial; microbiome; predictive modeling; programs; sex; sound; statistical center; tool

The Data Management and Analysis Core (Core D) will provide support for the projects and other cores in all aspects of study design and data management and analysis. This POl focuses on the characterization of specific adiposity phenotypes by imaging techniques, the identification of predictors of the phenotypes among lifestyle and biomarker variables derived from measurements in various biological dimensions (genefics, metabolomics, microbiome), and testing the association of the predictors, as proxy measures, with the risk of several cancers. The study subjects are participants in the well-established Multiethnic Cohort (MEC) study. The experienced biostatisticians and bioinformaticians of Core D collectively have extensive experience with the MEC data and design, and the types of data and analyses needed to support the aims of the P01. Specific aims include (1) to enhance the complex MEC informatics system to identify cohort members to approach for recruitment, to track their progress through the study, and to monitor receipt, storage and shipment of all biologic materials, (2) to pre-process high through-put data from genetic variation, metabolomics and gut microbiome in order to reduce the dimensionality and provide the most appropriate data for analysis, and (3) to develop and execute analysis plans for each of the research projects of this P01 application, in conjunction with the Project Leaders. In particular, appropriate statistical tools and software will be used to find the most predictive models of the adiposity phenotype under study and for the association studies of these predictors with cancer risk. Heterogeneity will be examined and tested across the ethnic groups in the prediction and association models.

数据管理和分析核心（核心D）将为项目和其他核心在研究设计以及数据管理和分析的各个方面提供支持。该 POl 重点关注通过成像技术表征特定肥胖表型，识别生活方式和生物标志物变量中表型的预测因子，这些变量源自不同生物维度（遗传学、代谢组学、微生物组）的测量，并测试预测因子的关联，如代理措施，与 多种癌症的风险。研究对象是成熟的多种族队列 (MEC) 研究的参与者。 Core D 经验丰富的生物统计学家和生物信息学家在 MEC 数据和设计以及支持 P01 目标所需的数据和分析类型方面拥有丰富的经验。具体目标包括 (1) 增强复杂的 MEC 信息学系统，以确定要招募的队列成员，跟踪他们的研究进展，并监控接收情况， 所有生物材料的储存和运输，(2) 预处理来自遗传变异、代谢组学和肠道微生物组的高通量数据，以降低维度并提供最合适的分析数据，以及 (3) 开发和与项目负责人一起执行此 P01 申请的每个研究项目的分析计划。特别是，将使用适当的统计工具和软件来寻找所研究的肥胖表型的最具预测性的模型，以及这些预测因子与癌症风险的关联研究。将在预测和关联模型中检查和测试不同种族群体的异质性。