Matched Design with Sensitivity Analysis for Observational Survival Data in Cardiovascular Patient Management using EMR Data

至事件发生时间是临床诊断和治疗效果评估中普遍存在的结局指标， 许多疾病领域，包括卒中（至卒中时间）、呼吸系统（至哮喘恶化首次用药时间） 和睡眠疾病（与失眠相关的死亡率）。风险率在生存分析中常见 因为它对瞬时风险有方便的解释。风险比（HR）通常用作效应 在两个治疗组之间进行比较时，主要是由于流行的考克斯比例风险 (PH)模型然而，人力资源管理系统容易受到选择偏差的影响，并且不具有可折叠性，这使得人力资源管理系统成为一个值得怀疑的系统。 边际因果效应测度限制性平均生存时间（RMST）是一种替代指标，定义为 直至固定时间点的生存曲线下面积。RMST差异是一个更充分的因果效应度量 比人力资源，因为（i）它是一个可折叠的措施，从而避免了边际和条件之间的差异 （二）它不依赖于PH假设;（三）它基本上是一个简单的平均差异 解释。RMST最近已成为随机试验中治疗效果的流行指标。然而，在这方面， 缺乏用于观察生存数据的RMST方法学的发展。这项提案的目的是 发展一个全面的基于匹配的RMST差异估计策略来推断因果效应 观察生存数据，并应用这些工具来评估直接口服 抗凝剂（DOAC）与华法林对心血管事件风险的影响。 我们计划开发基于倾向得分匹配的RMST估计方法和相应的 敏感性分析，不依赖于强有力的结果建模假设。匹配方法将使用 模拟区组随机设计的最优匹配集算法和渐近有效的 匹配后的推理过程将通过考虑匹配中引入的相关性来开发。 建立在匹配数据的基础上，敏感性分析将解决未测量的 混杂因素与暴露和结果之间的关系，以解释观察到的效应。 在二次数据分析中，我们将应用我们的方法来检验使用DOAC具有较低 复合心血管事件的风险，包括卒中、静脉血栓栓塞、心肌梗死，以及 死亡，使用电子病历（EMR）数据。我们还将探讨与以下因素相关的亚组因果效应： 性别和种族，以审查潜在的健康差距问题。我们提出的工作不仅会产生新的， 有效的研究方法来估计观察性生存数据中的因果关系，而且还提高了 了解不同的抗凝药物如何影响使用大型二次 数据库我们的通用方法学将广泛适用于研究心脏、肺、血液和心血管系统中的生存数据。 和睡眠疾病的治疗，以及差异研究。这也将使临床研究人员能够严格 使用越来越多的可用现实世界数据确定因果证据。

Time-to-event is a ubiquitous outcome measure in clinical diagnosis and assessment of therapeutic effects in many disease areas including stroke (time-to-stroke), respiratory (time-to-first medication for worsening asthma) and sleep diseases (time-to-insomnia-related mortality). The hazard rate is commonly seen in survival analysis as it has the convenient interpretation of instantaneous risk. The hazard ratio (HR) is routinely used as an effect measure when comparing between two treatment groups, largely due to the popular Cox proportional hazards (PH) model. However, the HR is vulnerable to selection bias and not collapsible, which make it a questionable marginal causal effect measure. Restricted mean survival time (RMST) is an alternative measure, defined as the area under the survival curve up to a fixed time point. RMST difference is a more adequate causal effect measure than the HR because (i) it is a collapsible measure, thus avoids discrepancy between marginal and conditional effects; (ii) it does not depend on the PH assumption; (iii) it is essentially a mean difference with simpler interpretation. RMST has become a popular metric of treatment effects in randomized trials recently. However, the development of RMST methodology for observational survival data is lacking. The goal of this proposal is to develop a comprehensive matching-based RMST difference estimation strategy to infer causal effects in observational survival data, and apply such tools to evaluate causal effects of direct oral anticoagulants (DOAC) vs. warfarin on the risk of cardiovascular events in a secondary data analysis. We plan to develop propensity score matching-based RMST estimation methodology and corresponding sensitivity analysis, which do not rely on strong outcome modeling assumptions. The matching method will use an optimal algorithm to create matched sets to mimic a block randomized design and an asymptotically valid post-matching inferential procedure will be developed by accounting for the correlation introduced in matching. Built upon the matched data, the sensitivity analysis will address how much association an unmeasured confounder would need to have with both the exposure and the outcome, to explain away the observed effect. In the secondary data analysis, we will apply our methods to examine the hypothesis that using DOAC has lower risk of composite cardiovascular events including stroke, venous thromboembolism, myocardial infarction, and death, using electronic medical record (EMR) data. We will also explore subgroup causal effects related to gender and race to examine potential health disparity issues. Our proposed work will not only result in novel and valid research methodology for estimating causal effects in observational survival data, but also advance the understanding of how different anticoagulant drugs would impact patient outcomes using a large secondary database. Our general-purpose methodology will be widely applicable to study survival data in heart, lung, blood and sleep disease treatment, and disparity research. This will also enable clinical researchers to rigorously identify causal evidence using increasingly available real-world data.