Biostatistics and Quantitative Sciences Shared Resource

生物统计学与数量科学共享资源 生物统计学和定量科学共享资源（BQS-SR）是佛罗里达健康癌症大学 中心（UFHCC）管理共享资源，其目标是为UFHCC成员提供集中的 生物统计学、生物信息学、计算生物学、临床试验支持和定量分析方面的专业知识资源 确保癌症研究的科学严谨性。BQS-SR通过提供 跨学科的合作支持成员在其研究的各个阶段的3个研究计划。 BQS-SR协助进行实验设计、样本量和功效评估、随机化和分层 程序，研究执行，数据分析，以及在理解，预防， 和治疗癌症。BQS-SR由Lee（CCPS）指导，他拥有超过20年的会员经验， 在NCI指定的癌症中心的生物统计共享资源的领导者，在单位领导的支持下， 生物信息学和计算生物学、临床试验和统计基因组学。BQS-SR由12名员工组成 人员（共7.5 UFHCC支持的FTE），包括4个生物统计学系，1个生物信息学系，5个 生物统计学工作人员和2名生物信息学工作人员。此外，BQS-SR作为联络人， 现有的校园范围内的专业定量科学家和UFHCC成员之间的媒人，以支持 外部资助的癌症研究项目的特殊需要。BQS-SR教师与每个 研究计划和疾病网站组，以确保随时访问UFHCC成员。BQS-SR 通过与研究人员、受训人员和研究人员的合作和教育，促进严谨性和可重复性 通过研讨会，个别会议，并为癌症研究人员量身定制的短期课程。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通过以下方式推进定量科学： 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成员支持了222个独特的UFHCC用户（138个具有同行 通过286份赠款申请审查了资金），并通过 SRMC和DISC。自2018年Lee重组以来，BQS-SR已收到320份支持请求， 113个独特的UFHCC成员，约34%与赠款提案有关，28%用于IIT开发，20%用于数据 支持同行评审出版物的分析。BQS-SR成员是340项癌症相关研究的共同作者。 同行评审出版物15%，IF ≥10。根据UFHCC战略计划Momentum 2027， BQS-SR未来的计划包括实施癌症健康公平性的扩展组随机试验设计 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，并引领基于人工智能的机器学习/深度学习的创新应用 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.