Deep Learning-based Emulation Analysis: Methodological Developments and Case Studies

基于深度学习的仿真分析：方法发展和案例研究

• 批准号: 10515491
• 负责人: Shuangge Ma
• 金额: $ 12.56万
• 依托单位: YALE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-08-15 至 2024-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10515491
• 关键词: Address; Adopted; Age; Anticoagulants; Architecture; Atrial Fibrillation; Cardiovascular Diseases; Case Study; Clinical; Clinical Trials; Clinical Trials Design; Collaborations; Complement; Computer software; Computerized Medical Record; Coronary heart disease; Data; Data Analyses; Databases; Development; Devices; Elderly; Enrollment; Ensure; FDA approved; Future; Gold; Heart failure; Implantable Defibrillators; Infrastructure; Injury to Kidney; Medical Records; Medicare; Methodology; Methods; Modeling; Observational Study; Oral; Patients; Performance; Persons; Pharmaceutical Preparations; Population; Primary Prevention; Procedures; Property; Publishing; Pythons; Randomized Clinical Trials; Reproducibility; Research; Risk; Safety; Solid; Spironolactone; Statistical Models; Survival Analysis; Techniques; Testing; United States Department of Veterans Affairs; acute coronary syndrome; analysis pipeline; antagonist; base; clinical practice; clinically significant; comparative effectiveness; comparative efficacy; cooperative study; data warehouse; deep learning; design; experience; flexibility; immune function; improved; innovation; insurance claims; loss of function; mortality; programs; prototype; relative effectiveness; simulation; software development; success; survival outcome; treatment effect

Project Summary To objectively quantify the relative effectiveness of drugs, devices, and treatment procedures on survival outcomes of cardiovascular diseases (CVDs), rigorously designed and executed randomized clinical trials (RCTs) remain as the gold standard. However, for many problems, RCTs either have failed or are not feasible. Luckily, the fast development of electronic medical record (EMR) and insurance claims databases makes it possible to mine a large amount of observational data and efficiently complement RCTs. Among the available observational data analysis techniques that aim to draw RCT-type conclusions, emulation has emerged as especially attractive, given its trial-like architecture, interpretability, and scalability. It has been applied to CVDs for over twenty years and led to many important findings. This study has two aims. The first aim is to develop a deep learning (DL)-based emulation analysis pipeline, methods, and software. Most of the existing emulation analyses are based on “classic” regression techniques. Very recently, our group was the first to develop DL-based emulation analysis with application to CVDs. Compared to regression, DL excels by having superior model fitting and flexibly accommodating unspecified nonlinear effects. Built on our recent success, this project will methodologically significantly advance by developing cutting-edge DL-based emulation analysis with more effective estimation (that has the much- desired robustness property and significantly improved stability and interpretability), comprehensive and valid inference (which is essential for making definitive conclusions on treatment effects but missing in most DL studies), and friendly software (to facilitate broad utilization). This methodological effort can substantially expand the scope of emulation analysis, deep learning, causal inference, observational data analysis, and medical record/insurance claims data analysis. The second aim is to conduct two clinically highly significant case studies. The first case study is on evaluating the effect of ICD (Implantable Cardioverter Defibrillator) on all-cause mortality in the VA (Department of Veterans Affairs) elderly population. The clinical trial targeting at addressing this problem failed because of low enrollment. As part of the VA CAUSAL Initiative, emulation was proposed as a viable solution to “replace” the trial. The second case study is on evaluating the comparative efficacy of Rivaroxaban versus Dabigatran on the mortality of AF (atrial fibrillation) patients in the Medicare population, for which an RCT is unlikely with both drugs FDA-approved and already popularly used. Beyond directly informing clinical practice, research under this aim can also complement and advance the VA CAUSAL Initiative as well as serve as a prototype for future applications of the proposed approach.

项目概要 客观量化药物、设备和治疗程序对生存的相对有效性 心血管疾病（CVD）的结果，严格设计和执行的随机临床试验 （随机对照试验）仍然是黄金标准，但是，对于许多问题，随机对照试验要么失败了，要么不可行。 幸运的是，电子病历（EMR）和保险理赔数据库的快速发展使得 可以挖掘大量观测数据并有效补充现有的随机对照试验。 旨在得出 RCT 类型结论的观察数据分析技术，仿真已经出现 鉴于其类似试验的架构、可解释性和可扩展性，它特别有吸引力，它已应用于 CVD。 二十多年来，取得了许多重要的发现。 本研究有两个目标，第一个目标是开发基于深度学习 (DL) 的仿真分析。 大多数现有的仿真分析都是基于“经典”回归。 最近，我们的团队率先开发了基于 DL 的仿真分析并应用于 与回归相比，深度学习的优势在于具有卓越的模型拟合能力和灵活的适应能力。 基于我们最近的成功，该项目将在方法上取得显着进展。 通过开发基于深度学习的尖端仿真分析和更有效的估计（具有更多 所需的稳健性并显着提高稳定性和可解释性），全面且有效 推断（这对于对治疗效果做出明确的结论至关重要，但在大多数 DL 中都缺失） 研究）和友好的软件（以促进广泛利用），这种方法论的努力可以大大扩展。 仿真分析、深度学习、因果推理、观察数据分析和医学的范围 记录/保险索赔数据分析。第二个目标是进行两个具有临床意义的案例研究。 第一个案例研究是评估 ICD（植入式心脏复律除颤器）对所有原因的影响 该临床试验旨在解决 VA（退伍军人事务部）老年人群的死亡率问题。 由于入学率低，这个问题失败了。作为 VA CAUSAL Initiative 的一部分，仿真被提议为： “替代”试验的可行解决方案第二个案例研究是评估试验的比较功效。 利伐沙班与达比加群对医疗保险人群中 AF（心房颤动）患者死亡率的影响， 对于这两种已获得 FDA 批准且已普遍使用的药物，不可能进行随机对照试验（RCT）。 临床实践，在此目标下的研究也可以补充和推进 VA CAUSAL Initiative 作为所提出方法的未来应用的原型。