Inference for Cumulative Incidences and Treatment Effects in Randomized Controlled Trials with Time-to-Event Outcomes under ICH E9 (E1)
arxiv(2024)
摘要
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.
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