Biostatistics and Quantitative Sciences Shared Resource

生物统计学和定量科学共享资源：摘要 生物统计和定量科学共享资源(BQS-SR)是佛罗里达大学的健康癌症 中心(UFCC)托管的共享资源，其目标是为UFCC成员提供集中化的 生物统计学、生物信息学、计算生物学、临床试验支持和量化专业知识的资源 科学以确保癌症研究的科学严谨性。BQS-SR通过提供 在研究的所有阶段为3个研究项目的成员提供跨学科协作支持。 BQS-SR有助于实验设计、样本量和能力评估、随机化和分层 程序、研究执行、数据分析和发布在理解、预防、 以及癌症的治疗。BQS-SR由Lee(CCPS)指导，具有20年的成员经验或 NCI指定的癌症中心生物统计共享资源的领导者，并得到单位领导人的支持 生物信息学和计算生物学、临床试验和统计基因组学。BQS-SR有12名工作人员 人员(共有7.5名UFCC支持的FTE)，包括4名生物统计学系、1名生物信息学系、5名 生物统计人员科学家和2名生物信息学人员科学家。此外，BQS-SR还担任联络员和 现有全校专业量化科学家与UFCC成员之间的牵线搭桥，以支持 外部资助的癌症研究项目的专门需求。BQS-SR教职员工与每个 研究计划和疾病网站小组，以确保UFCC成员可以随时访问。BQS-SR 通过与调查人员、受训人员和研究人员合作并对他们进行教育，促进严谨和可重复性 通过为癌症研究人员量身定做的研讨会、个人会议和短期课程。BQS-SR确保 通过参加IIT智库、科学审查和监测委员会确保临床研究的完整性 (SRMC)和数据完整性和安全委员会(DISC)。BQS-SR通过以下方式推进了量化科学 开发新的方法，包括新的微生物组数据分析方法和可视化 单细胞RNA-Seq数据。自2016年以来，BQS-SR成员支持222个UFHCC唯一用户(138个具有对等- 审查资金)通过286份拨款申请，并审查了411项干预临床试验方案 SRMC和光盘。自2018年Lee重组以来，BQS-SR已收到来自 113个独特的UFCC成员，其中约34%涉及赠款提案，28%涉及IIT开发，20%涉及数据 支持同行评议出版物的分析。BQS-SR成员是340项与癌症相关的研究的共同作者 同行评议的出版物15%与IF≥10。根据UFCC战略计划，Momentum 2027， BQS-SR未来的计划包括为癌症健康公平实施扩大的分组随机试验设计 和差异研究，为单细胞RNA-Seq数据开发新的图形方法/统计方法 分析并引领基于创新人工智能的机器学习/深度学习的应用 用于癌症研究中的诊断、预后和预测模型开发。

BIOSTATISTICS AND QUANTITATIVE SCIENCES SHARED RESOURCE: ABSTRACT Biostatistics and Quantitative Sciences Shared Resource (BQS-SR) is a University of Florida Health Cancer Center (UFHCC) managed shared resource whose objective is to provide UFHCC members with a centralized resource for biostatistics, bioinformatics, computational biology, clinical trial support, and expertise in quantitative science to ensure scientific rigor in cancer research. The BQS-SR meets this objective by providing interdisciplinary collaborative support to members across the 3 research programs at all stages of their research. BQS-SR assists with experimental design, sample size and power evaluation, randomization and stratification procedures, study execution, data analysis, and publication of new findings in the understanding, prevention, and treatment of cancer. The BQS-SR is directed by Lee (CCPS), with >20 years of experience as a member or leader of a biostatistical shared resource at NCI-Designated Cancer Centers, with support from Unit Leaders for bioinformatics and computational biology, clinical trials, and statistical genomics. BQS-SR is staffed by 12 personnel (a total of 7.5 UFHCC-supported FTE), comprised of 4 Biostatistics faculty, 1 Bioinformatics faculty, 5 Biostatistics staff scientists, and 2 Bioinformatics staff scientists. Additionally, BQS-SR serves as a liaison and matchmaker between existing campus-wide specialized quantitative scientists and UFHCC members to support specialized needs of externally funded cancer research projects. BQS-SR faculty are aligned to each of the Research Programs and Disease Site Groups to ensure ready accessibility to UFHCC members. BQS-SR promotes rigor and reproducibility by collaborating with and educating investigators, trainees, and research staff through seminars, individual sessions, and short courses tailored to cancer researchers. BQS-SR ensures the integrity of clinical research by participating in the IIT Think Tank, Scientific Review and Monitoring Committee (SRMC), and the Data Integrity and Safety Committee (DISC). The BQS-SR advances quantitative science by developing new methodologies including novel microbiome data analysis approaches and visualization for single-cell RNA-Seq data. Since 2016, BQS-SR members supported 222 unique UFHCC users (138 with peer- reviewed funding) through 286 grant submissions and reviewed 411 interventional clinical trial protocols through the SRMC and DISC. Since reorganized by Lee in 2018, the BQS-SR has received 320 support requests from 113 unique UFHCC members, with ~34% related to grant proposals, 28% for IIT development, and 20% for data analysis supporting peer-reviewed publications. BQS-SR members were co-authors on 340 cancer-relevant peer-reviewed publications 15% with IF ≥10. In alignment with the UFHCC strategic plan, Momentum 2027, the BQS-SR future plans include implementing expanded group-randomized trial designs for cancer health equity and disparities studies, developing novel graphical approaches/statistical methods for single-cell RNA-Seq data analysis, and leading the application of innovative artificial intelligence-based machine learning/deep learning for diagnostic, prognostic, and prediction model development in cancer research.