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New statistical methods and software for modeling complex multivariate survival data with large-scale covariates

用于对具有大规模协变量的复杂多变量生存数据进行建模的新统计方法和软件

基本信息

批准号：
10631139
负责人：
Ying Ding
金额：
$ 30.45万
依托单位：
UNIVERSITY OF PITTSBURGH AT PITTSBURGH
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10631139
关键词：
Acute Address Age related macular degeneration Algorithms Alzheimer&apos s Disease Bilateral Cessation of life Clinical Clinical Management Collection Complex Computer software Data Development Diagnostic Disease Disease Progression Event Eye Eye diseases Future Genetic Image Joints Label Literature Machine Learning Measures Methodology Methods Modeling Modernization Neural Network Simulation Observational Study Otitis Media Outcome Patients Prevention Procedures Recommendation Recurrence Research Risk Risk Assessment Risk Factors Shapes Software Tools Statistical Methods Structure Subgroup Testing Therapy trial Time Treatment Efficacy Universities Women&apos s Health comorbidity computerized tools deep neural network disorder prevention disorder risk ear infection flexibility frailty high dimensionality high risk hormone therapy improved individualized medicine innovation large scale data loss of function machine learning method neural network neuroimaging novel personalized predictions personalized risk prediction precision medicine primary endpoint randomized, clinical trials semiparametric statistical and machine learning statistics success survival outcome therapy development treatment effect treatment planning trial design

项目摘要

ABSTRACT In randomized clinical trials and observational studies, multivariate outcomes are increasingly used as co- primary endpoints to study complex diseases or clinical outcomes comprised of co-morbidities. Some modern studies also collect large-scale genetics or image data for the potential of individualized risk prediction and precision medicine development. Moreover, the precise event times for non-fatal events are sometimes unobservable because the event status can only be determined at intermittent assessment times. The non- fatal events may also be censored by fatal events (i.e., death) which results in semi-competing risks data. The complex multivariate survival outcome together with large-scale covariates pose great analytical challenges for such studies. Inspired by the challenges and opportunities met in our motivating studies for two bilateral diseases, Age-related Macular Degeneration (AMD) and Acute Otitis Media (AOM), as well as the wealthy data from the hormone therapy trial in Women Health Initiative (WHI) and the Alzheimer Disease Neuroimaging Initiative (ADNI), the broad aim of this proposal is to develop new statistical and machine learning methods and computational tools for analyzing such data. First, we will develop a class of semiparametric copula models that flexibly joint model the multivariate survival data without ad-hoc data simplification. A rigorous goodness- of-fit test will be proposed for model diagnostics. Next, using the top risk factors identified from the semiparametric copula model as inputs, we will develop a multivariate survival deep neural network to predict individualized disease risk proﬁles over time, which are critical for personalized disease prevention and clinical management. Then, based on fundamental multiple testing principles, we propose a novel simultaneous inference procedure to identify and infer subgroups with enhanced treatment efficacy under our proposed copula framework. Finally, we will develop a meta-learner framework to estimate individualized treatment effects and to give treatment recommendation rules. The novel methodology will be immediately applied to the ongoing AMD, AOM and AD research at the University of Pittsburgh, as well as the data from WHI and ADNI to facilitate novel analyses for identifying risk factors and assessing treatment effects on disease progression, recurrence, or prevention. The methodology advances will be applicable to a broad range of studies with similar data features. In summary, the successful completion of the project will lead to a comprehensive methodological framework with ready-to-use software packages, which have the potential to fundamentally improve the current practice in analyzing such studies, and thus to enhance the discovery of disease risk factors, improve the prediction of disease progression profiles, and increase the success of precision medicine.

摘要