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Data Management and Statistics Core

数据管理和统计核心

基本信息

批准号：
10675656
负责人：
Jonathan L Haines
金额：
$ 32.48万
依托单位：
CLEVELAND CLINIC LERNER COM-CWRU
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-01 至 2026-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10675656
关键词：
Adopted Agreement Alzheimer&apos s Disease Alzheimer&apos s Disease Core Center Alzheimer&apos s disease related dementia Area Biological Markers Biometry Clinical Clinical Data Collaborations Collection Communities Complication Computational Biology Data Data Analyses Data Management Resources Data Set Databases Enrollment Environment Error Sources Family Foundations Funding Generations Genetic Genetic Diseases Genomics Goals Human Resources Image Individual Informatics Infrastructure Institution Lead Longitudinal Studies Machine Learning Measures Methods National Institute on Aging Neurodegenerative Disorders Paper Participant Policies Positioning Attribute Privacy Procedures Process Recording of previous events Records Research Research Design Research Personnel Resources Sampling Secure Security System Training Universities data acquisition data format data integration data management data mining data storage site design electronic data electronic data sharing empowerment experience follow-up human disease improved research study software infrastructure statistics success virtual

项目摘要

PROJECT SUMMARY The success of research studies in any human disease, but particularly Alzheimer's disease (AD) and AD-related dementias (ADRD), is critically dependent on the collection, generation, management, analysis, and interpretation of large, varied, and detailed data sets. The overall goal of the Data Management and Statistical Core (DMSC) is to empower AD/ADRD researchers to acquire, manage, analyze, and interpret their data. To do this, we leverage existing institutional infrastructure and provide the necessary expertise for the Cleveland Alzheimer's Disease Center (CADRC). This goal will be accomplished through three specific aims. Specific Aim 1 provides a comprehensive data management platform for the collection, aggregation, and management of the relevant data. We will leverage the infrastructure of the Cleveland Institute for Computational Biology (CICB), which has developed a safe and secure computational environment for data capture and management that is available to all the participating institutions (the SHED [Safely Held Electronic Data] data management system). The SHED uses a tier III secure computational environment along with both computational and policy access and privacy controls. The system easily integrates a wide variety of data formats and provides methods for participant and sample tracking for longitudinal studies. Comprehensive training for CADRC personnel is provided. Specific Aim 2 enhances interactivity with the other cores, investigators, and external databases. Through the efforts of the Administrative Core, participating institutions have institutional agreements to capture and share electronic data through the SHED. All investigators and staff across the CADRC institutions have controlled access to the SHED. We have put into place processes internally for interacting with each of the CADRC cores, and externally for interactions with databases and users such as the National Alzheimer's Coordinating Center (NACC) and the National Institute for Aging Genetics of Alzheimer's Disease Data Storage site (NIAGADS). Specific Aim 3 provides biostatistical, study design, and data analysis support to the CADRC. The DMSC provides support for these activities through access to a wide range of expertise including biostatistical (Drs. Tatsuoka and Feng), genomic (Drs. Haines and Feng), and imaging (Dr. Tatsuoka). Dr. Feng also brings expertise in data integration and machine learning to the CADRC. Substantial technical support will be provided by a master's level analyst. The DMSC is well positioned to accommodate the data management and data analysis needs of the CADRC, its investigators, and the broader AD/ADRD research community.

项目总结