Novel Statistical Methods for Risk Communication of Atrial Fibrillation

项目总结/摘要 房颤（AF）无法治愈，因此预防AF和风险沟通是关键。在风险预测中 在模型中，风险因素和AF之间的关联通常表示为风险比。但 风险比难以解释。一种新的度量标准，限制平均生存时间（RMST）的差异， 提供了有临床意义的解释，有利于风险沟通。RCST的差异 两个曝光组之间的平均时间是由于曝光而没有AF丢失的平均时间。与危险相比， 风险组之间RMST的差异提供了一个绝对的衡量标准， 风险因素和AF。通过报告RMST改善风险沟通将直接影响 心血管公共卫生尽管RMST很有吸引力，但在观察性研究中仍然报告不足 解释。原因之一是风险预测模型和复杂数据的RMST方法存在差距 这在心血管研究中很常见。需要开发新的RMST方法， 更大的灵活性，以应对心血管研究中的统计挑战。 我们建议解决观察性研究中RMST方法的差距。我们的总体目标是 改进估计RMST的统计方法，并通过以下方法提高我们对AF流行病学的理解： 这些新方法。目标1是开发新的统计指标和数据可视化， AF风险预测模型的外部验证。目标2是开发适应时间的RMST方法- 不同的风险因素，如体重指数。目的3是发展RMST方法的竞争风险 死亡我们将使用模拟研究评估我们的新统计方法的性能，并说明我们的 方法使用来自心脏病研究（FHS）和社区动脉粥样硬化风险的AF数据 研究（ARIC）。此外，我们将通过以下方式向更大的研究社区提供我们的新方法： 生产R包。我们专注于AF，但我们的方法可用于各种疾病。 推进RMST方法将使研究人员能够更频繁地报告RMST， 传达房颤风险。我的导师团队在房颤的流行病学研究方面具有出色的经验， 生存数据的统计方法，并致力于支持我在我的培训和专业 发展我们制定了一项培训计划，其中包括预防战略的课程， 心血管疾病的生理学、分子机制和流行病学， 终身数据和授权写入的方法。通过这个奖学金，我将发展的技能，以实现我的长期- 长期目标是成为一名具有心血管疾病专业知识的独立研究人员。在这次奖学金之后， 我计划通过获得博士后职位并申请K 01资助，继续推进风险沟通 为个体参与者数据荟萃分析和联合生存曲线开发RMST方法。

Project Summary/Abstract There is no cure for atrial fibrillation (AF), thus prevention of AF and risk communication are key. In risk prediction models, associations between risk factors and AF are commonly expressed as hazard ratios. However, the hazard ratio is challenging to interpret. A novel metric, the difference in restricted mean survival time (RMST), offers a clinically meaningful interpretation and is advantageous for risk communication. The difference in RMSTs between two exposure groups is the mean time without AF lost due to the exposure. In contrast to the hazard ratio, the difference in RMST between risk groups provides an absolute measure of the association between a risk factor and AF. Improved risk communication by reporting the RMST will have a direct impact on cardiovascular public health. The RMST remains underreported in observational studies despite its appealing interpretation. One reason is there are gaps in RMST methods for risk prediction models and complex data scenarios, which are common in cardiovascular research. There is a need to develop new RMST methods with greater flexibility to address statistical challenges in cardiovascular research. We propose to address gaps in RMST methodology for observational studies. Our overall objective is to improve statistical methods for estimating the RMST and improve our understanding of AF epidemiology with these new methods. Aim 1 is to develop new statistical metrics and data visualizations for the internal and external validation of AF risk prediction models. Aim 2 is to develop RMST methods that accommodate time- varying risk factors, such as body mass index. Aim 3 is to develop RMST methods for the competing risk of death. We will assess the performance of our new statistical methods using simulation studies, and illustrate our methods using AF data from the Framingham Heart Study (FHS) and the Atherosclerosis Risk in Communities Study (ARIC). Additionally, we will make our novel methods available to the greater research community by producing R packages. We focus on AF, but our methods can be used for a wide range of diseases. Advancing RMST methods will allow researchers to report the RMST more frequently when communicating AF risk. My mentoring team has outstanding experience in epidemiological research of AF and statistical methods for survival data, and is committed to supporting me in my training and professional development. We have designed a training plan which includes coursework in the prevention strategies, physiology, molecular mechanisms, and epidemiology of cardiovascular disease, and workshops in advanced methods for lifetime data and grant-writing. Through this fellowship, I will develop the skills to achieve my long- term goal of becoming an independent researcher with expertise in cardiovascular disease. After this fellowship, I plan continue advancing risk communication by obtaining a postdoctoral position and applying for a K01 grant to develop RMST methods for individual participant data meta-analysis and combined survival curves.