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

• 批准号: 10453875
• 负责人: Ying Ding
• 金额: $ 30.13万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 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/10453875
• 关键词: Acute; Address; Age related macular degeneration; Algorithms; Alzheimer' s Disease; Bilateral; Cessation of life; Clinical; Clinical Management; Collection; Complex; Computer software; Custom; 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; Randomized Clinical Trials; Recommendation; Recurrence; Research; Risk; Risk Factors; Shapes; Software Tools; Statistical Methods; Structure; Subgroup; Testing; Therapy trial; Time; Treatment Efficacy; Universities; Women' s Health; base; 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; semiparametric; statistical and machine learning; success; survival outcome; therapy development; treatment effect; treatment planning; trial design

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.

抽象的 在随机临床试验和观察性研究中，多变量结果越来越多地被用作联合研究 研究由一些现代并发症组成的复杂疾病或临床结果的主要终点。 研究还收集大规模遗传学或图像数据，以实现个性化风险预测和 此外，非致命事件的精确事件时间有时是准确的。 不可观察，因为事件状态只能在间歇评估时间确定。 致命事件也可能受到致命事件（即死亡）的审查，从而导致半竞争风险数据。 复杂的多变量生存结果与大规模协变量一起构成了巨大的分析挑战 受到我们在两项双边激励研究中遇到的挑战和机遇的启发。 疾病、年龄相关性黄斑变性（AMD）和急性中耳炎（AOM）以及丰富的数据 来自女性健康倡议 (WHI) 中的激素治疗试验和阿尔茨海默病神经影像学 倡议（ADNI），该提案的广泛目标是开发新的统计和机器学习方法， 首先，我们将开发一类半参数联结模型。 灵活地联合建模多变量生存数据，无需临时数据简化。 接下来，将使用从模型中识别出的主要风险因素来进行模型诊断。 半参数 copula 模型作为输入，我们将开发一个多元生存深度神经网络来预测 随着时间的推移，个性化的疾病风险概况，这对于个性化疾病预防和临床至关重要 然后，基于基本的多重测试原则，我们提出了一种新颖的同步管理。 推断程序，用于识别和推断根据我们提出的治疗效果增强的亚组 最后，我们将开发一个元学习器框架来估计个体化治疗。 效果并给出治疗建议规则将立即应用于。 匹兹堡大学正在进行的 AMD、AOM 和 AD 研究，以及来自 WHI 和 ADNI 的数据 促进新的分析，以确定风险因素并评估治疗对疾病进展的影响， 该方法的进步将适用于广泛的研究。 综上所述，该项目的成功完成将带来全面的数据特征。 具有现成可用软件包的方法框架，有可能从根本上 改进当前分析此类研究的实践，从而加强疾病风险的发现 因素，改善疾病进展情况的预测，并提高精准医疗的成功率。