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

项目摘要 客观量化药物、器械和治疗程序对生存率的相对有效性 心血管疾病（CVD）的结局，严格设计和执行的随机临床试验 （RCT）仍然是黄金标准。然而，对于许多问题，RCT要么失败，要么不可行。 幸运的是，电子病历（EMR）和保险索赔数据库的快速发展使其成为一个重要的信息来源。 可以挖掘大量的观察性数据，并有效地补充RCT。在现有的 观察数据分析技术，旨在得出RCT类型的结论，仿真已经出现， 特别有吸引力，因为它具有类似试验的体系结构、可解释性和可扩展性。已应用于心血管疾病 20多年来，他取得了许多重要的发现。 这项研究有两个目的。第一个目标是开发基于深度学习（DL）的仿真分析 流水线、方法和软件。现有的仿真分析大多基于“经典”回归 技术.最近，我们的团队率先开发了基于DL的仿真分析， 心血管疾病。与回归相比，DL具有上级模型拟合和灵活适应性 未指明的非线性效应在我们最近成功的基础上，这个项目将在方法上大大推进 通过开发尖端的基于DL的仿真分析和更有效的估计（具有更高的 期望的鲁棒性和显著提高的稳定性和可解释性），全面和有效 推断（这对于得出治疗效果的明确结论至关重要，但在大多数DL中缺失） 研究）和友好的软件（以促进广泛使用）。这种方法上的努力可以大幅扩展 仿真分析、深度学习、因果推理、观察数据分析和医疗的范围 记录/保险索赔数据分析。第二个目的是进行两个临床高度重要的病例研究。 第一个案例研究是关于评价ICD（植入式心律转复除颤器）对全因心律失常的影响 VA（退伍军人事务部）老年人口的死亡率。临床试验旨在解决 这个问题因为低入学率而失败。作为VA CAUSAL倡议的一部分，提出了以下模拟： 一个可行的解决方案来“取代”审判。第二个案例研究是关于评估 利伐沙班与达比加群对医疗保险人群中AF（房颤）患者死亡率的影响， 这两种药物都是FDA批准并已广泛使用的，不太可能进行RCT。除了直接告知 在临床实践中，这一目标下的研究也可以补充和推进VA CAUSAL倡议 作为所提出的方法的未来应用的原型。

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.