In randomized controlled trials (RCTs) that focus on time-to-event outcomes, intercurrent events can arise in two ways: as semi-competing events, which modify the hazard of the primary outcome events, or as competing events, which make the definition of the primary outcome events unclear. Although five strategies have been proposed in the ICH E9 (R1) addendum to address intercurrent events in RCTs, these strategies are not easily applicable to time-to-event outcomes when aiming for causal interpretations. In this study, we show how to define, estimate, and make inferences concerning objectives that have causal interpretations within these contexts. Specifically, we derive the mathematical formulations of the causal estimands corresponding to the five strategies and clarify the data structure needed to identify these causal estimands. Furthermore, we introduce nonparametric methods for estimating and making inferences about these causal estimands, including the asymptotic variance of estimators and hypothesis tests. Finally, we illustrate our methods using data from the LEADER Trial, which aims to investigate the effect of liraglutide on cardiovascular outcomes.

翻译：在关注时间-事件结局的随机对照试验中，中间事件可能以两种方式出现：作为半竞争事件，其会改变主要结局事件的风险；或作为竞争事件，其使得主要结局事件的定义变得不明确。尽管ICH E9 (R1)增补中提出了五种策略来处理RCT中的中间事件，但当旨在进行因果解释时，这些策略不易应用于时间-事件结局。在本研究中，我们展示了如何在这些背景下定义、估计和推断具有因果解释的目标。具体而言，我们推导了与五种策略相对应的因果估计量的数学公式，并阐明了识别这些因果估计量所需的数据结构。此外，我们介绍了用于估计和推断这些因果估计量的非参数方法，包括估计量的渐近方差和假设检验。最后，我们使用旨在研究利拉鲁肽对心血管结局影响的LEADER试验数据来说明我们的方法。