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

利用生物标志物对合并 PTSD 的酒精使用障碍进行个性化治疗

• 批准号: 10473680
• 负责人: EUGENE M LASKA
• 金额: $ 13.68万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-20 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10473680
• 关键词: Algorithms; Analysis of Variance; Bioinformatics; Biological; Biological Markers; Biostatistics Core; Classification; Collaborations; Communication; Confounding Factors (Epidemiology); Consultations; Data; Data Analyses; Data Analytics; Data Collection; Data Management Resources; Data Science; Data Scientist; Data Storage and Retrieval; Documentation; Ensure; Experimental Designs; Factor Analysis; Health; High Performance Computing; Human; Individual; Lasso; Leadership; Machine Learning; Measures; Methodology; Methods; Mining; Modeling; Mus; Outcome; Periodicity; Play; Population; Post-Traumatic Stress Disorders; Prediction of Response to Therapy; Procedures; Quality Control; Randomized Clinical Trials; Relapse; Reporting; Research; Research Project Grants; Role; Sample Size; Sampling; Secure; Services; Statistical Methods; Statistical Models; Techniques; Testing; Time; Translating; Update; Variant; Visualization; alcohol comorbidity; alcohol use disorder; analytical method; base; causal model; clinical trial analysis; comorbidity; cost efficient; data centers; data cleaning; data integration; data integrity; data management; data quality; data sharing; data visualization; demographics; design; experience; improved; novel; novel strategies; personalized medicine; predictive marker; prognostic; random forest; tool; topiramate; treatment response

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.

项目总结/摘要 分析和生物统计学核心（ABC）将提供具有成本效益的，响应性和综合性数据 为中心项目提供管理、统计和分析支持。它将提供方便的数据访问， 有针对性的咨询和促进跨学科和综合研究的合作。它将利用， 适应和/或开发新的和有效的分析方法和设计，使生物学的研究 促进个性化医疗的机制。它将在整合调查结果方面发挥重要作用 使用定量和定性的方法进行跨项目研究。尤金拉斯卡博士将担任主任， 博士Carole Siegel是Core的副主任，另外还有一位统计/数据科学家Meng博士 钱和一位数据管理专家。ABC的具体目标是：1）就 详细负责中心项目的实验设计、数据收集、数据质量控制和维护 集中记录这些工作; 2）数据管理和存储服务， 安全的数据共享、数据集成和数据可视化; 3）分析来自每个中心和跨中心的数据 研究项目酌情应用最先进的生物信息学，统计建模，机器学习， 因果分析工具和算法。4)新的分析方法，以进一步告知个性化 药将应用或开发新的计算和分析方法，包括新的 基于生物标志物的临床试验数据分析范式，用于概率的因果建模 治疗反应，可以在其应用中将该领域推向更个性化的医疗。 ABC工作人员精通最先进的数据分析统计方法，包括ANOVA，混合 模型和回归模型的应用，轨迹分析，以检查随时间的变化，潜在的 变量在因素分析中的使用和生存时间方法在其使用检查发作和复发。 他们在潜在的预后指标方面有处理不平衡样本的经验， 对于批次效应，控制混杂变量，包括人口统计学、合并症和健康状况 条件ABC工作人员熟练使用数据分析/挖掘技术进行分类， 聚类，特别是随机森林，以及在回归中识别重要变量的方法 例如Lasso、岭回归和弹性网回归。