Parsimonious Models for Survival Data

生存数据的简约模型

• 批准号: 8545192
• 负责人: Chris Fraley
• 金额: $ 7.28万
• 依托单位: INSILICOS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-14 至 2015-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8545192
• 关键词: Address; Affect; Area; Biological; Biological Assay; Biological Markers; Biology; Biomedical Research; Cardiovascular Diseases; Clinical; Clinical Data; Complex; Cox Models; Data; Data Set; Databases; Detection; Development; Diagnostic; Diagnostic tests; Disease; Eating; Evaluation; Event; Exhibits; Explosion; Genomics; Goals; In Vitro; Individual; Laboratories; Lasso; Life; Literature; Malignant Neoplasms; Measurement; Measures; Medical Research; Methods; Modeling; Monitor; Myocardial Infarction; Neoplasm Metastasis; Outcome; Pathway interactions; Patients; Performance; Pharmaceutical Preparations; Phase; Public Health; ROC Curve; Research; Research Contracts; Research Project Grants; Risk; Sampling; Services; Small Business Innovation Research Grant; Speed; Statistical Methods; Stroke; Study models; Support System; Survival Analysis; Techniques; Technology; Testing; Therapeutic; Time; Uncertainty; Validation; Work; analytical tool; anticancer research; base; cancer therapy; clinical practice; cohort; cost; data mining; design; disease diagnosis; drug development; hazard; improved; indexing; interest; ion mobility; new technology; novel diagnostics; novel strategies; prognostic; prototype; screening

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.

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生物标志物检测的问题。目的是研究新的方法，从由许多可能不相关或冗余的测量组成的数据，一个高度预测和可解释的模型，只涉及少量的测量。这些新方法将用于建模受试者的事件时间（如中风、心脏病发作或癌症转移）。提出的方法将与现有的方法进行比较，这些方法试图使用相对较少的测量值来建模生存（事件时间）数据。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，这些数据的测量量比样本要多得多。的相关性。尽管今天的先进技术为革命性的临床实践提供了可能，但用于从大量数据中提取信息的分析工具尚未充分开发，无法对潜在生物学进行有针对性的探索。这个项目