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：在具有时间-事件结局的随机对照试验中，并发事件作为半竞争/竞争事件出现，可能影响结局的风险或导致结局定义不明确。尽管ICH E9（R1）附录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至时间-事件数据情境，以研究具有严格明确含义的因果效应。在本研究中，我们展示了如何在此类情境下定义、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，我们推导出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与推断方法。我们利用LEADER试验（研究利拉鲁肽对心血管结局的影响）阐明了我们的方法。对多个终点以及结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治疗效应。