Biostatistics/Bioinformatics Core

生物统计学和生物信息学核心将支持#年软组织肉瘤中孢子的研究人员 他们研究工作的计算和统计方面，包括临床研究的设计和分析 试验、实验室实验、分子图谱、测序以及综合基因组分析。 在临床试验设计阶段，核心成员将与主要研究人员一起进行方案审查。 在此审查的基础上，将提供议定书的统计部分，概述主要的科学 研究对象、研究对象、主要和次要终点、实验设计、分析计划、 以及以概率形式证明合理的目标样本量。在试验结束时，将进行数据分析 执行以评估议定书中规定的主要和次要终点的结果。在临床前阶段 研究，核心成员将协助制定试验设计和分析 在研究结束时对数据进行解释。核心还将协助大规模的分子 人肉瘤样本的特征，这涉及到分析RNA表达，microRNA 表达、突变和DNA拷贝数变化以确定肿瘤特征和药物的预测因素 雷普森，和生存。核心成员将使用双样本t检验和Cox比例风险模型来 确定信息的五个特征。不同类型的分子数据将被整合并进行路径分析 以阐明促进肿瘤发生和肿瘤表型的途径。风险预测 将使用基于这些特征的COX比例风险模型来开发工具

The Biostatistics & Bioinformatics Core will support investigators of the SPORE in Soft Tissue Sarcoma in the computational and statistical aspects of their research efforts, including the design and analysis of clinical trials, laboratory experiments, molecular profiling, and sequencing, as well as integrated genomic analyses. In the clinical trial design phase, a core member will conduct a protocol review with the principal investigator. Based on this review, a statistical section for the protocol will be provided, outlining major scientific objecfives, population to be studied, primary and secondary endpoints, experimental design, analysis plans, and a targeted sample size justified in probabilisfic terms. At the conclusion of the trial, data analyses will be performed to assess outcomes of the primary and secondary endpoints stated in the protocol. In preclinical studies, core members will assist in the formulation of the experimental design and in the analysis and interpretafion of the data at the conclusion of the study. The core will also assist in the large-scale molecular characterizafion of human sarcoma samples, which involves analyzing RNA expression, microRNA expression, mutafions, and DNA copy number changes to identify predictors of tumor characterisfics, drug repsonse, and survival. Core members will use two-sample t-tests and Cox proportional hazards models to identify informafive features. The different types of molecular data will be integrated and pathway analysis performed to elucidate the pathways that contribute to tumorigenesis and tumor phenotype. Risk predicfion tools will be developed using Cox proportional hazards models based on these features