Novel Statistical Methods for Risk Communication of Atrial Fibrillation

项目摘要/摘要 心房颤动(房颤)是无法治愈的，因此预防房颤和风险沟通是关键。在风险预测中 模型中，危险因素和房颤之间的关联通常用危险比来表示。然而， 危险比率很难解释。一种新的度量，限制平均生存时间(RMST)的差异， 提供了临床上有意义的解释，有利于风险沟通。RMST之间的差异 两个暴露组之间是没有因暴露组而导致房颤消失的平均时间。与危险形成对比的是 比率，风险组之间的RMST差异提供了对 危险因素与房颤。通过报告RMST来改善风险沟通将对 心血管公共卫生。尽管RMST很有吸引力，但在观察性研究中仍未得到充分报道 释义。一个原因是，RMST方法在风险预测模型和复杂数据方面存在差距 场景，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RMST观测研究方法中的空白。我们的总体目标是 改进估计RMST的统计方法，提高我们对房颤流行病学的理解 这些新方法。目标1是开发新的统计指标和数据可视化，用于内部和 房颤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。目标3是开发针对竞争风险的RMST方法 死亡。我们将使用模拟研究来评估我们的新统计方法的性能，并说明我们的 方法使用来自弗雷明翰心脏研究(FHS)的房颤数据和社区的动脉粥样硬化风险 研究(ARIC)。此外，我们将通过以下方式将我们的新方法提供给更多的研究社区 制作R包。我们专注于房颤，但我们的方法可以用于广泛的疾病。 先进的RMST方法将允许研究人员在以下情况下更频繁地报告RMST 传达房颤风险。我的指导团队在房颤和房颤的流行病学研究方面有出色的经验 生存数据的统计方法，并致力于支持我的训练和专业 发展。我们设计了一项培训计划，其中包括预防战略方面的课程， 心血管疾病的生理学、分子机制和流行病学 生命周期数据和赠款写入的方法。通过这一奖学金，我将发展技能，以实现我的长期- 学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。在这次联谊之后， 我计划通过获得博士后职位和申请K01奖学金来继续推进风险沟通 建立用于个体受试者资料荟萃分析和组合生存曲线的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.