Analyzing the causal mediation of semi-competing risks has become important in medical research. Semi-competing risks refers to a scenario wherein an intermediate event may be censored by a primary event but not vice versa. Causal mediation analyses decompose the effect of an exposure on the primary outcome into an indirect (mediation) effect: an effect mediated through a mediator, and a direct effect: an effect not through the mediator. Here we proposed a model-based testing procedure to examine the indirect effect of the exposure on the primary event through the intermediate event. Under the counterfactual outcome framework, we defined a causal mediation effect using counting process. To assess statistical evidence for the mediation effect, we proposed two tests: an intersection–union test (IUT) and a weighted log-rank test (WLR). The test statistic was developed from a semi-parametric estimator of the mediation effect using a Cox proportional hazards model for the primary event and a series of logistic regression models for the intermediate event. We built a connection between the IUT and WLR. Asymptotic properties of the two tests were derived, and the IUT was determined to be a size \(\alpha \) test and statistically more powerful than the WLR. In numerical simulations, both the model-based IUT and WLR can properly adjust for confounding covariates, and the Type I error rates of the proposed methods are well protected, with the IUT being more powerful than the WLR. Our methods demonstrate the strongly significant effects of hepatitis B or C on the risk of liver cancer mediated through liver cirrhosis incidence in a prospective cohort study. The proposed method is also applicable to surrogate endpoint analyses in clinical trials.

分析半竞争风险的因果关系在医学研究中变得很重要。半竞争风险是指一种情况，其中中间事件可能会被主要事件审查，反之亦然。因果中介分析将暴露对主要结果的影响分解为间接（中介）效应：通过中介介导的影响和直接影响：不通过中介的影响。在这里，我们提出了一个基于模型的测试程序来检查暴露通过中间事件对主要事件的间接影响。在反事实结果框架下，我们使用计数过程定义了因果中介效应。为了评估中介效应的统计证据，我们提出了两个测试：交集测试 (IUT) 和加权对数秩测试 (WLR)。检验统计量是从中介效应的半参数估计中发展而来的，对主要事件使用 Cox 比例风险模型，对中间事件使用一系列逻辑回归模型。我们在 IUT 和 WLR 之间建立了连接。推导出两个检验的渐近性质，IUT被确定为一个大小\(\alpha\)测试和统计上比 WLR 更强大。在数值模拟中，基于模型的 IUT 和 WLR 都可以针对混杂协变量进行适当调整，并且所提出方法的 I 类错误率得到很好的保护，IUT 比 WLR 更强大。在一项前瞻性队列研究中，我们的方法证明了乙型或丙型肝炎对通过肝硬化发病率介导的肝癌风险的强烈显着影响。所提出的方法也适用于临床试验中的替代终点分析。