Semiparametric Analysis of Big Censored Data

大删失数据的半参数分析

• 批准号: 10391489
• 负责人: DANYU LIN
• 金额: $ 48.22万
• 依托单位: UNIV OF NORTH CAROLINA CHAPEL HILL
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-04-21 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10391489
• 关键词: Acquired Immunodeficiency Syndrome; Address; Algorithms; Big Data; Biomedical Research; Cardiovascular Diseases; Cessation of life; Characteristics; Chronic Disease; Cloud Computing; Communication; Computer software; Country; Cox Models; Cox Proportional Hazards Models; Data; Data Security; Data Set; Dimensions; Disease; Documentation; Environment; Event; Funding; Genetic; Goals; Individual; Infrastructure; Life; Malignant Neoplasms; Mathematics; Maximum Likelihood Estimate; Memory; Methods; Modeling; Modernization; Modification; National Heart, Lung, and Blood Institute; Performance; Process; Property; Proportional Hazards Models; Public Health; Random Allocation; Research; Research Project Grants; Savings; Scheme; Survival Analysis; Testing; Time; Trans-Omics for Precision Medicine; United States; Update; Work; base; big biomedical data; biobank; cluster computing; expectation; genome-wide analysis; high dimensionality; innovation; interest; multimodal data; novel; open source; parallel computer; precision medicine; preconditioning; premature; prevent; programs; semiparametric; simulation; sound; statistics; theories; tool; user-friendly

Project Summary The broad, long-term objectives of this project are to develop semiparametric regression methods for analyzing censored data, which are commonly encountered in biomedical research on chronic diseases. This renewal application is focused on addressing the computational challenges in the analysis of big data involving hun- dreds of thousands to tens of millions of individuals with thousands to tens of millions of variables. The specific aims are to develop: (1) a communication-efficient, distributed boosting algorithm based on semiparametric effi- cient score functions for fitting the Cox proportional hazards model to a wide variety of big censored data; (2) a communication-efficient, distributed boosting algorithm that embeds a random feature-set selection scheme into variable selection in high-dimensional settings; (3) a communication-efficient, distributed boosting algorithm for fitting a Cox model with latent factors to multiple types of high-dimensional features with missing values; and (4) a distributed EM algorithm that incorporates both the preconditioned conjugate-gradient method for matrix inver- sion and a novel modification of the Laplace approximation to numerical integration for fitting a random-effect Cox model with a large number of genetically related individuals. Each of these aims addresses important new chal- lenges arising from today's big biomedical studies. The proposed methods and algorithms are based on likelihood and other sound statistical principles. The desired asymptotic properties of the estimators will be established rig- orously through innovative use of modern empirical process theory and other advanced mathematical tools. The proposed methods and algorithms will be evaluated extensively through simulation studies mimicking real data and tested in the cloud computing environment, which provides high data security guarantees and scalable com- puting infrastructures. In addition, the methods and algorithms will be applied to our ongoing biomedical studies, including the NHLBI Trans-Omics for Precision Medicine program and the UK Biobank. Finally, efficient, reliable, and user-friendly open-source software with proper documentation will be produced. The overall impact of the proposed work will be to create new paradigms for survival analysis, advance biomedical research in the United States and other countries, and accelerate the search for effective strategies to prevent and treat cardiovascular diseases, cancers, AIDS, and other diseases of utmost importance to global public health.

项目摘要 该项目的广泛长期目标是开发用于分析的半参数回归方法 经过审查的数据，这些数据通常在慢性疾病的生物医学研究中遇到。这个更新 应用的重点是应对分析涉及涉及的大数据的计算挑战 成千上万至数千万到数千万变量的人被挖了。特定的 目的是开发：（1）基于半参数效率的沟通良好的，分布式的增强算法 CIENT得分功能可以使COX比例危害模型拟合到各种大型公民数据； （2）a 通信效果，分布式增强算法，将随机特征集选择方案嵌入 高维设置中的可变选择； （3）一种通讯良好的，分布式的增强算法 将具有潜在因素的COX模型拟合到具有缺失值的多种类型的高维特征； （4） 一种分布式EM算法，既包含用于矩阵Inver- 拉普拉斯近似与数值整合的新型修饰，以拟合随机效应Cox 具有大量一般相关个体的模型。这些目标都解决了重要的新chal- 今天的大型生物医学研究产生了长度。提出的方法和算法基于可能性 和其他健全的统计原则。将建立估计量的所需渐近性能 通过创新的现代经验过程理论和其他高级数学工具的创新使用。 提出的方法和算法将通过模拟真实数据的模拟研究进行广泛评估 并在云计算环境中进行了测试，该环境提供了高数据安全保证和可扩展的com- 放置基础设施。此外，方法和算法将应用于我们正在进行的生物医学研究， 包括NHLBI的精密医学计划和英国生物库。最后，有效，可靠， 将生产具有适当文档的用户友好开源软件。总体影响 拟议的工作将是为生存分析创建新的范式，在统一的生物医学研究 各州和其他国家，并加速寻找有效的策略来预防和治疗心血管 疾病，癌症，艾滋病和其他对全球公共卫生最重要的疾病。