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（Core D）将在研究设计和数据管理和分析的各个方面为项目和其他核心提供支持。本POl侧重于通过成像技术表征特定的肥胖表型，从各种生物维度（遗传学，代谢组学，微生物组学）的测量中确定生活方式和生物标志物变量中表型的预测因子，并测试预测因子的相关性，作为替代措施，与