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: 10237284
• 负责人: EUGENE M LASKA
• 金额: $ 10.06万
• 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 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10237284
• 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.

项目摘要/摘要 分析和生物统计学核心（ABC）将提供具有成本效益，响应和综合数据 对中心项目的管理以及统计和分析支持。它将轻松访问数据， 有针对性的咨询并促进跨学科和综合研究的合作。它将利用， 适应和/或开发新颖有效的分析方法和设计，以研究生物学 导致促进个性化医学的机制。它将在整合发现中发挥重要作用 在项目之间使用定量和定性方法。尤金·拉斯卡（Eugene Laska）博士将担任董事 核心副主任Carole Siegel博士由其他统计/数据科学家Meng博士组成 Qian和数据管理专家。 ABC的具体目的是提供：1）就 中心项目，数据收集，数据质量控制和维护的实验设计的详细信息 这些努力的集中文件； 2）数据管理和存储服务有效， 有安全数据共享，数据集成和数据可视化； 3）分析来自中心的每个中心的数据 研究项目适用于适当的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，以进一步告知个性化 药品。新颖的计算和分析方法将被应用或开发，包括新的 用于分析基于生物标志物的临床试验数据的范例，用于因果建模的概率 可以在其应用中使用的治疗反应，以将领域迈向更个性化的医学。 ABC员工精通最先进的统计方法，用于分析包括方差分析，混合的数据 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 它们在用于检查和复发的用途中使用中的变量和生存时间方法。 他们有根据潜在的预后措施处理不平衡样本的经验，调整 对于批处理效果，控制混淆变量，包括人口统计学，合并症和健康 状况。 ABC员工熟练使用数据分析/采矿技术进行分类和 聚集最特别的森林和用于识别回归中重要变量的方法 例如拉索，山脊回归和弹性净回归。