标题： 基于影响函数的分布式Kaplan-Meier分析及其在新冠状病毒和新冠状病毒疫苗不良事件中的应用 显示英文标题

标题： Distributed Kaplan-Meier Analysis via the Influence Function with Application to COVID-19 and COVID-19 Vaccine Adverse Events

摘要： 在新冠疫情期间，由于缺乏关于罕见结果的及时和高质量数据，监管决策受到了阻碍。 研究感染和疫苗接种后的罕见结果需要进行多中心观察性研究，其中共享个体层面的数据是一个隐私问题。 在本文中，我们进行了一项关于新冠感染和新冠疫苗接种后血栓栓塞事件的多中心观察性研究，而无需共享个体层面的数据。 我们通过开发一种新的分布式学习方法来构建Kaplan-Meier（KM）曲线和逆倾向加权KM曲线，并进行统计推断。 我们依次在各站点使用KM影响函数更新曲线，这是一种衡量观测值应如何改变我们估计方向的指标，因此可以在不访问先前数据的情况下纳入新观测值。 我们在模拟中展示了我们的分布式估计器是无偏的，并且效率与合并数据估计器相当。 将我们的方法应用于Beaumont Health、Spectrum Health和密歇根医学的数据，我们发现与首次新冠疫苗相比，SARS-CoV-2感染后血液凝块的协变量调整发病率要高得多（3.13%，95% CI: [2.93, 3.35]），而首次新冠疫苗为0.08%，95% CI: [0.08, 0.09]）。 这表明疫苗对新冠相关血栓的保护作用超过了疫苗相关不良事件的风险，并展示了分布式生存分析在为时间敏感决策提供可操作证据方面的潜力。 显示英文摘要

摘要： During the COVID-19 pandemic, regulatory decision-making was hampered by a lack of timely and high-quality data on rare outcomes. Studying rare outcomes following infection and vaccination requires conducting multi-center observational studies, where sharing individual-level data is a privacy concern. In this paper, we conduct a multi-center observational study of thromboembolic events following COVID-19 and COVID-19 vaccination without sharing individual-level data. We accomplish this by developing a novel distributed learning method for constructing Kaplan-Meier (KM) curves and inverse propensity weighted KM curves with statistical inference. We sequentially update curves site-by-site using the KM influence function, which is a measure of the direction in which an observation should shift our estimate and so can be used to incorporate new observations without access to previous data. We show in simulations that our distributed estimator is unbiased and achieves equal efficiency to the combined data estimator. Applying our method to Beaumont Health, Spectrum Health, and Michigan Medicine data, we find a much higher covariate-adjusted incidence of blood clots after SARS-CoV-2 infection (3.13%, 95% CI: [2.93, 3.35]) compared to first COVID-19 vaccine (0.08%, 95% CI: [0.08, 0.09]). This suggests that the protection vaccines provide against COVID-19-related clots outweighs the risk of vaccine-related adverse events, and shows the potential of distributed survival analysis to provide actionable evidence for time-sensitive decision making.