Leveraging biomarkers for personalized treatment of alcohol use disorder comorbid with PTSD

项目摘要/摘要 分析和生物统计核心(ABC)将提供具有成本效益、反应灵敏和综合的数据 为中心项目提供管理、统计和分析支持。它将提供对数据的轻松访问， 开展有针对性的咨询，促进跨学科和综合研究的协作。它将利用， 适应和/或开发新的和有效的分析方法和设计，使生物学研究成为可能 促进个性化医疗的机制。它将在整合调查结果方面发挥强大的作用 使用定量和定性方法跨越项目。尤金·拉斯卡博士将担任导演， Carole Siegel博士，核心副主任，另有一名统计/数据科学家孟博士 钱学森是一位数据管理专家。ABC的具体目标是：1)就 中心项目的实验设计、数据收集、数据质量控制和维护的详细情况 集中记录这些工作；2)数据管理和存储服务，可实现高效和 安全的数据共享、数据集成和数据可视化；3)分析每个中心和跨中心的数据 研究项目适当地应用最先进的生物信息学、统计建模、机器学习、 以及因果分析工具和算法。4)新的分析方法，进一步提供个性化信息 医药。将应用或开发新的计算和分析方法，包括新的 基于生物标记物的临床试验数据分析范例 治疗反应，可以在其应用中服务，将该领域推向更个性化的医学。 ABC员工精通分析数据的最先进的统计方法，包括ANOVA、MIXED 模型和回归模型的应用，轨迹分析检验随时间的变化，潜伏期 因子分析中使用的变量和检查发病和复发的生存时间方法。 他们在处理不平衡样本方面有经验，包括潜在的预测指标、调整 对于批次效应，控制混杂变量，包括人口统计、合并症和健康 条件。ABC员工熟练使用数据分析/挖掘技术进行分类和 最特别是随机森林的聚类法以及在回归中识别重要变量的方法 如套索回归、岭回归、弹性网回归等。

Project Summary/Abstract The Analytics and Biostatistics Core (ABC) will provide cost efficient, responsive and integrative data management and statistical and analytic support for Center projects. It will provide easy access to data, targeted consultations and facilitate collaboration for cross disciplinary and integrative research. It will utilize, adapt and/or develop novel and efficient analytic methods and designs that allow the study of biological mechanisms leading to the facilitation of personalized medicine. It will play a strong role in integrating findings across projects using both quantitative and qualitative approaches. Dr. Eugene Laska will be the director and Dr. Carole Siegel the deputy director of the Core staffed by an additional statistical/data scientist, Dr. Meng Qian and a data management expert. The specific aims of the ABC are to provide: 1) consultation on the details of the experimental designs of Center projects, data collection, data quality control and to maintain centralized documentation of these efforts; 2) data management and storage services enabling efficient and secured data sharing, data integration and data visualization; 3) analysis of data from each and across Center research projects applying as appropriate state-of-the-art bioinformatics, statistical modeling, machine learning, and causal analysis tools and algorithms. 4) new analytic methodologies to further inform personalized medicine. Novel computational and analytical approaches will be applied or developed including a new paradigm for the analysis of clinical trial data based on biomarkers for causal modelling of the probability of treatment response that can serve in its application to move the field towards more personalized medicine. ABC staff are well versed in state of the art statistical methods for analyzing data including ANOVAs, mixed models and the application of regression models, trajectory analysis to examine variation over time, latent variables in their use in factor analysis and survival time methods in their use to examine onset and relapse. They have experience in handling unbalanced samples in terms of potentially prognostic measures, adjusting for batch effects, controlling for confounding variables including demographics, comorbidities and health conditions. ABC staff has proficiency in the use of data analytic/mining techniques for classification and clustering most particularly random forests and in methods for identifying important variables in in regression such as lasso, ridge regression and elastic net regression.