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

使用 EMR 数据对心血管患者管理中的观察性生存数据进行匹配设计和敏感性分析

• 批准号: 10731172
• 负责人: Bo Lu
• 金额: $ 11.81万
• 依托单位: OHIO STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-01 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10731172
• 关键词: Accounting; Adopted; Algorithms; Anticoagulants; Area; Asthma; Cardiovascular system; Cessation of life; Clinical; Clinical assessments; Communities; Comparative Effectiveness Research; Complex; Computer software; Computerized Medical Record; Cox Proportional Hazards Models; Data; Data Analyses; Databases; Development; Diagnostics Research; Disease; Disparity; Drug usage; Effectiveness; Event; Funding Mechanisms; Gender; Goals; Heart Diseases; Hematological Disease; Lung diseases; Mean Survival Times; Measures; Methodology; Methods; Modeling; Myocardial Infarction; National Heart, Lung, and Blood Institute; Observational Study; Oral; Outcome; Outcome Measure; Patient-Focused Outcomes; Patients; Performance; Pharmaceutical Preparations; Procedures; Race; Randomized; Reporting; Research; Research Methodology; Research Personnel; Research Project Grants; Risk; Selection Bias; Sleep Disorders; Sleeplessness; Stratification; Stroke; Subgroup; Survival Analysis; Therapeutic; Therapeutic Effect; Therapeutic Research; Time; Warfarin; Work; cardiovascular risk factor; clinical diagnosis; data registry; design; effectiveness evaluation; hazard; health disparity; method development; mortality; novel; primary endpoint; randomized trial; respiratory; simulation; tool; treatment effect; treatment group; venous thromboembolism

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.

事件时间是临床诊断和评估治疗作用的无处不在的结果指标 许多疾病领域，包括中风（势时间），呼吸道（哮喘恶化的时间） 和睡眠疾病（与诱因有关的死亡率）。危险率通常在生存分析中看到 因为它具有方便的瞬时风险解释。危险比（HR）通常用作效果 在两个治疗组之间进行比较时的测量，主要是由于流行的COX比例危害 （pH）模型。但是，人力资源容易受到选择偏见而不可折叠的攻击，这使它成为一个值得怀疑的 边际因果效应量度。受限的平均生存时间（RMST）是一种替代措施，定义为 生存曲线下的区域至固定时间点。 RMST差异是一种更加适当的因果效应量度 比人力资源部相比，因为（i）这是一个可折叠的措施，因此避免了边际和条件之间的差异 效果； （ii）它不取决于pH假设； （iii）本质上是平均差异，更简单 解释。 RMST最近在随机试验中已成为流行的治疗效果指标。然而， 缺乏用于观察生存数据的RMST方法的发展。该提议的目的是 制定基于匹配的RMST差异估计策略来推断因果关系 在观测生存数据中，并应用此类工具来评估直接口腔的因果关系 抗凝剂（DOAC）与华法林有关心血管事件的风险在次要数据分析中。 我们计划开发基于倾向分数匹配的RMST估计方法和相应的 灵敏度分析不依赖于强烈的结果建模假设。匹配方法将使用 一种最佳算法来创建匹配集以模仿块随机设计和渐近有效的块 通过考虑匹配中引入的相关性，将开发匹配后推论过程。 基于匹配的数据，灵敏度分析将解决未计量的关联 混杂因素需要与暴露和结果同时解释观察到的效果。 在二级数据分析中，我们将采用我们的方法来检验以下假设：使用DOAC较低 复合心血管事件的风险，包括中风，静脉血栓栓塞，心肌梗塞和 使用电子病历（EMR）数据死亡。我们还将探讨与 性别和种族检查潜在的健康差异问题。我们提出的工作不仅会导致新颖，并且 有效的研究方法，用于估计观察性生存数据中因果关系的作用，但也推进了 了解不同的二级抗凝药会如何影响患者的结果 数据库。我们的通用方法将广泛用于研究心脏，肺，血液中的生存数据 和睡眠疾病治疗和差异研究。这也将使临床研究人员严格 使用越来越多的现实世界数据来确定因果证据。