Parsimonious Models for Survival Data

描述(由申请人提供)：这个小型企业创新研究项目解决了临床和高通量数据中的生物标记物检测问题。我们的目标是研究新的方法，从由许多可能不相关或冗余的测量组成的数据中，挖掘一个只涉及少量测量的高度预测和可解释的模型。这些新方法将被用于对受试者的事件发生时间(如中风、心脏病发作或癌症转移)进行建模。建议的方法将与现有的方法进行比较，现有方法试图使用相对较少的方法来模拟生存(事件发生时间)数据。要分析的数据将包括离子迁移率和来自一大批心血管疾病队列的临床数据，以及来自癌症研究的高通量基因组数据，这些数据的测量比样本多得多。关联性。尽管今天的先进技术为临床实践提供了革命性的可能性，但可用于从如此数量的DAA中提取信息的分析工具还没有充分开发出来，以有针对性地探索潜在的生物学。这个项目 直接解决了FDA所称的IVDMIA(体外诊断多元指数分析)透明和可解释的需求，从而为识别、表征和验证临床诊断和药物开发决策点的生物标记物的公司提供了一个改善分析服务或产品的机会。拟议的项目将产生用于简约生物标记物检测的强大方法，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。

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.