Xu Yanxun, Scharfstein Daniel, Müller Peter, Daniels Michael
Department of Applied Mathematics and Statistics, Johns Hopkins University, 3400 N. Charles Street, Baltimore, MD 21218, USA
Department of Biostatistics, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, USA.
Biostatistics. 2022 Jan 13;23(1):34-49. doi: 10.1093/biostatistics/kxaa008.
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.
我们开发了一种贝叶斯非参数（BNP）方法，用于评估随机试验中治疗的因果效应，其中非终末事件可能被终末事件截尾，但反之则不然（即半竞争风险）。基于主分层的思想，我们定义了一种用于治疗对非终末事件因果效应的新估计量。我们引入了由一个敏感性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。我们进行了模拟研究，并使用来自一项脑癌试验的数据说明了我们的方法。实现我们模型和算法的R代码可在https://github.com/YanxunXu/BaySemiCompeting下载。
