In randomized controlled trials (RCT) with time-to-event outcomes, intercurrent events occur as semi-competing/competing events, and they could affect the hazard of outcomes or render outcomes ill-defined. Although five strategies have been proposed in ICH E9 (R1) addendum to address intercurrent events in RCT, they did not readily extend to the context of time-to-event data for studying causal effects with rigorously stated implications. In this study, we show how to define, estimate, and infer the time-dependent cumulative incidence of outcome events in such contexts for obtaining causal interpretations. Specifically, we derive the mathematical forms of the scientific objective (i.e., causal estimands) under the five strategies and clarify the required data structure to identify these causal estimands. Furthermore, we summarize estimation and inference methods for these causal estimands by adopting methodologies in survival analysis, including analytic formulas for asymptotic analysis and hypothesis testing. We illustrate our methods with the LEADER Trial on investigating the effect of liraglutide on cardiovascular outcomes. Studies of multiple endpoints and combining strategies to address multiple intercurrent events can help practitioners understand treatment effects more comprehensively.

翻译：在时间至事件结局的随机对照试验中，并发事件以半竞争/竞争事件的形式出现，它们可能影响结局的风险或导致结局定义不清。尽管ICH E9 (R1) 增补文件提出了五种策略来处理随机对照试验中的并发事件，但这些策略并未直接扩展到时间至事件数据背景下，以进行具有严格假设的因果效应研究。在本研究中，我们展示了如何在此类背景下定义、估计和推断结局事件随时间变化的累积发生率，以获得因果解释。具体而言，我们推导了这五种策略下科学目标（即因果估计量）的数学形式，并阐明了识别这些因果估计量所需的数据结构。此外，我们通过采用生存分析方法（包括渐近分析的分析公式和假设检验）总结了这些因果估计量的估计和推断方法。我们以LEADER试验为例，展示了利拉鲁肽对心血管结局的影响。关于多个终点和组合策略处理多个并发事件的研究，有助于从业者更全面地理解处理效应。