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中文摘要
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TREC生物信息学和生物统计共享资源核心将为UCSD TREC项目、共享资源和核心的信息学、统计、分析和数据共享需求提供统一支持。 这一共享的资源核心将利用摩尔加州大学圣迭戈分校癌症中心的资源,该中心包括一个由5名教职员工和7名统计学家和信息学专家组成的有凝聚力的小组,他们致力于癌症中心的研究项目,拥有超过8FTE。该核心将协调和支持加州大学可持续发展学院TREC项目的数据库开发和维护。数据将被编程到摩尔斯加州大学圣迭戈分校癌症中心维护的安全关系数据库中。支持多种数据收集方式,如电话、基于网络的和电子CRF条目。核心将在TREC项目中进行广泛的自动数据验证和标准化监测和报告,包括协议遵从性、表格填写和应计报告、自动化中期数据质量检查和S,以及锁定和记录最终研究数据集,患者的机密性将受到HIPAA安全和隐私法规的保护。 生物统计和生物信息学共享资源还将为TREC项目提供统计支持,包括方案审查、研究设计、数据分析和手稿准备。最后,该核心将为试点项目和受训人员提供统计咨询。该中心将与TREC协调中心合作,确保UCSD TREC出版物和数据集的共享。
英文摘要
The TREC Bioinformatics and Biostatistics Shared Resource Core will provide unified support for informatics, statistical, analytic, and data sharing needs across the UCSD TREC projects, shared resources, and cores. This Shared Resource Core will leverage the resources of the Moores UCSD Cancer Center, which includes a cohesive group of 5 faculty and 7 staff statisticians and informatics specialists with over 8 FTE dedicated exclusively to Cancer Center research projects. The Core will coordinate and support database development and maintenance for UCSD TREC Projects. Data will be programmed into the secure relational database maintained by the Moores UCSD Cancer Center. Multiple data collection modalities such as telephone, webbased, and e-CRF entry are supported. The Core will undertake extensive automatic data validation and standardized monitoring and reporting across TREC projects, including protocol compliance, form completion and accrual reporting, automated interim data quality checl<s, and locking and documenting of finalized study data sets Patient confidentiality will be protected in compliance with HIPAA security and privacy regulations. The Biostatistics and Bioinformatics Shared Resource will also provide statistics support for TREC Projects including protocol review, study design, data analysis, and manuscript preparation. Finally, this Core will provide statistical consults for pilot projects and trainees. The Core will collaborate with the TREC Coordinating Center to ensure sharing of UCSD TREC publications and datasets.
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Novel computational techniques to detect the relationship between sitting patterns and metabolic syndrome in existing cohort studies.
Developing and validating prognostic metabolomic signatures of diabetic kidney disease
Developing and validating prognostic metabolomic signatures of diabetic kidney disease
Core B- Biostat Core
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