We develop a Bayesian nonparametric (BNP) approach to evaluate the causal effect of treatment in a randomized trial where a nonterminal event may be censored by a terminal event, but not vice versa (i.e., semi-competing risks). Based on the idea of principal stratification, we define a novel estimand for the causal effect of treatment on the nonterminal event. We introduce identification assumptions, indexed by a sensitivity parameter, and show how to draw inference using our BNP approach. We conduct simulation studies and illustrate our methodology using data from a brain cancer trial. The R code implementing our model and algorithm is available for download at https://github.com/YanxunXu/BaySemiCompeting.

中文翻译：

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用于评估具有半竞争风险的随机试验中治疗的因果效应的贝叶斯非参数方法


我们开发了贝叶斯非参数（BNP）方法来评估随机试验中治疗的因果效应，其中非最终事件可能会被最终事件审查，但反之则不然（即半竞争风险）。基于主分层的思想，我们为治疗对非终结事件的因果效应定义了一个新的估计值。我们引入了由灵敏度参数索引的识别假设，并展示了如何使用我们的 BNP 方法进行推断。我们进行模拟研究，并使用脑癌试验的数据说明我们的方法。实现我们的模型和算法的 R 代码可在 https://github.com/YanxunXu/BaySemiCompeting 下载。