Semiparametric Analysis of Big Censored Data

大删失数据的半参数分析

• 批准号: 10615672
• 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 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10615672
• 关键词: Acceleration; 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; Learning; 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; big biomedical data; biobank; cluster computing; expectation; genome-wide analysis; high dimensionality; innovation; interest; multimodal data; novel; open source; parallel computer; precision medicine; 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.

项目概要 该项目的广泛、长期目标是开发半参数回归方法来分析 审查数据，这是慢性病生物医学研究中常见的情况。此次续订 应用程序的重点是解决涉及大数据分析的计算挑战 数千到数千万个人的数据，其中包含数千到数千万的变量。具体的 目标是开发：（1）一种基于半参数效率的通信高效的分布式增强算法 用于将 Cox 比例风险模型拟合到各种大审查数据的古老评分函数； (2) 一个 通信高效的分布式增强算法，将随机特征集选择方案嵌入到 高维设置中的变量选择； (3) 一种高效通信的分布式增强算法 将具有潜在因子的 Cox 模型拟合到具有缺失值的多种类型的高维特征；和（4） 分布式 EM 算法，结合了矩阵求逆的预处理共轭梯度法 和拉普拉斯近似数值积分的新颖修改，用于拟合随机效应 Cox 具有大量遗传相关个体的模型。这些目标中的每一个都解决了重要的新挑战 当今大型生物医学研究中产生的重大问题。所提出的方法和算法基于似然 以及其他合理的统计原则。估计器所需的渐近特性将被建立 通过创新地使用现代经验过程理论和其他先进的数学工具。这 所提出的方法和算法将通过模仿真实数据的模拟研究进行广泛评估 并在云计算环境下进行测试，提供高数据安全保障和可扩展的组件 投入基础设施。此外，这些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 Trans-Omics for Precision Medicine 项目和 UK Biobank。最后，高效、可靠、 并且将生产带有适当文档的用户友好的开源软件。总体影响 拟议的工作将是创建生存分析的新范式，推进美国的生物医学研究 各国和其他国家，加快寻找预防和治疗心血管疾病的有效策略 疾病、癌症、艾滋病和其他对全球公共卫生至关重要的疾病。