Parsimonious Models for Survival Data

描述（由申请人提供）：该小型企业创新研究项目解决了临床和高通量数据中生物标志物检测的问题。其目的是研究新的方法来确定，从数据组成的许多可能无关或冗余的测量，一个高度预测和解释的模型，只涉及少量的测量。这些新方法将被研究用于模拟受试者的事件发生时间（如中风，心脏病发作或癌症转移）。所提出的方法将与现有的方法进行比较，这些方法试图在生存（事件发生时间）数据建模中使用相对较少的测量。待分析的数据将包括来自大型心血管疾病队列的离子迁移率和临床数据，以及来自癌症研究的高通量基因组数据，这些数据的测量值比样本多得多。本案无关虽然今天的先进技术提供了革命性的临床实践的可能性，分析工具，可用于提取信息，从这一数额的daa尚未充分发展为有针对性的探索的基础生物学。这个项目 直接解决了FDA所称的IVDMIA（体外诊断多变量指数测定）透明和可解释的需求，因此是一个机会，可以改善为公司提供的分析服务或产品，这些公司可以识别，表征和验证临床诊断和药物开发决策点的生物标志物。拟议的项目将产生用于简约生物标志物检测的强大方法，这将加速开发更便宜，更有效的诊断测试，用于疾病诊断，治疗监测和治疗药物开发。 公共卫生相关性：在医学研究中，非常需要可以从一些观察到的特征准确预测事件（例如心脏病发作）的时间的预后模型。这些模型可用于建立新的诊断和筛选测试，并推进新的治疗方法。提出了时间-事件建模的新方法，这将加速更便宜和更有效的临床支持系统的开发，并对公共卫生产生深远的影响。

DESCRIPTION (provided by applicant): This Small Business Innovation Research project addresses the problem of biomarker detection in clinical and high-throughput data. The objective is to investigate new approaches for deter- mining, from data consisting of many possibly irrelevant or redundant measurements, a highly predictive and interpretable model that involves only a small number of measurements. These new methods will be studied for modeling subjects' time-to-event (such as stroke, heart attack, or metastasis in cancer). The proposed approaches will be compared with existing methods that attempt to use relatively few mea- surements in modeling survival (time-to-event) data. The data to be analyzed will include ion-mobility and clinical data from a large cardiovascular disease cohort, as well as high-throughput genomic data from cancer research with many more measurements than samples. Relevance. Although today's advanced technologies offer the possibility of revolutionizing clinical practice, the analytical tools available for extracting information from this amount of daa are not yet sufficiently developed for targeted exploration of the underlying biology. This project directly addresses the need to make what the FDA terms IVDMIA (In-Vitro Diagnostic Multivariate Index Assays) transparent and interpretable, and is thus an opportunity to improve analysis services or products provided to companies that identify, characterize, and validate biomarkers for clinical diagnostics and drug development decision points. The proposed project will produce robust methods for parsimonious biomarker detection that will speed the development of cheaper and more effective diagnostic tests for disease diagnosis, treatment monitoring, and therapeutic drug development. PUBLIC HEALTH RELEVANCE: There is a great need in medical research for prognostic models that can accurately predict time to an event, such as a heart attack, from a few observed features. These models can be used in establishing new diagnostic and screening tests, and in advancing new therapies. New methods for time-to-event modeling are proposed that will speed the development of cheaper and more effective clinical support systems, and have a far-reaching impact on public health.