标题： 用于重复事件模型的逆概率加权 显示英文标题

标题： Inverse Probability Weighting for Recurrent Event Models

摘要： 复发事件在许多疾病领域中是常见且重要的临床试验终点，例如心力衰竭中的心血管住院、多发性硬化症中的复发或哮喘中的恶化。 在试验过程中，患者可能会经历伴随事件，即在治疗分配之后发生的事件，这些事件会影响感兴趣结果的解释或存在性。 在许多情况下，假设伴随事件不会发生的情况下的治疗效果具有临床意义。 这种假设治疗效果的适当估计方法必须考虑复发事件过程和伴随事件的所有混杂因素。 在本文中，我们提出了针对复发事件中假设目标的估计量，并对基线和内部时间变化协变量进行了适当的调整。 具体而言，我们将逆概率加权（IPW）应用于复发事件分析中常用的林-韦-杨-英（LWYY）和负二项（NB）模型。 模拟研究显示，我们的方法在偏差和功效方面优于其他分析方法。 显示英文摘要

摘要： Recurrent events are common and important clinical trial endpoints in many disease areas, e.g., cardiovascular hospitalizations in heart failure, relapses in multiple sclerosis, or exacerbations in asthma. During a trial, patients may experience intercurrent events, that is, events after treatment assignment which affect the interpretation or existence of the outcome of interest. In many settings, a treatment effect in the scenario in which the intercurrent event would not occur is of clinical interest. A proper estimation method of such a hypothetical treatment effect has to account for all confounders of the recurrent event process and the intercurrent event. In this paper, we propose estimators targeting hypothetical estimands in recurrent events with proper adjustments of baseline and internal time-varying covariates. Specifically, we apply inverse probability weighting (IPW) to the commonly used Lin-Wei-Yang-Ying (LWYY) and negative binomial (NB) models in recurrent event analysis. Simulation studies demonstrate that our approach outperforms alternative analytical methods in terms of bias and power.