Semiparametric Analysis of Big Censored Data

项目摘要 这个项目广泛的长期目标是开发半参数回归方法来分析 审查数据，这是在慢性病的生物医学研究中常见的。此次续订 应用程序侧重于解决大数据分析中的计算挑战，这些大数据涉及HUN- 挖掘数千到数千万个个体，以及数千到数千万个变量。该物种fic 目标是：(1)基于半参数EFfi-fi的通信型分布式Boosting算法。 fi将COX比例风险模型应用于各种大的删失数据的分数函数；(2)a 基于通信的fi分布式提升算法，将随机特征集选择方案嵌入到 高维环境下的变量选择；(3)一种通信有效的fi分布式Boosting算法 fi将带潜在因子的COX模型应用于多种类型的高维缺失值特征； 一种分布式EM算法，它结合了矩阵求逆的预条件共轭梯度法. Sion和拉普拉斯逼近数值积分的一种新的Modifi方法，用于fi设置随机效应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- 从今天的大型生物医学研究中产生的冗长。所提出的方法和算法都是基于似然的 和其他合理的统计原则。估计的期望渐近性质将被建立。 通过创新运用现代经验过程理论和其他先进的数学工具。这个 所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的评估 并在云计算环境下进行了测试，提供了较高的数据安全保障和可伸缩的组件。 投放基础设施。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计划和英国生物库。最后，EFfi是有效的，可靠的， 并将制作用户友好的开放源码软件和适当的文档。全球经济一体化的总体影响 拟议的工作将是为生存分析创造新的范例，推动美国的生物医学研究 各国和其他国家，并加快寻找预防和治疗心血管疾病的有效战略 疾病、癌症、艾滋病和其他对全球公共卫生极为重要的疾病。

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.