Restricted mean survival time (RMST) is an intuitive summary statistic for time-to-event random variables, and can be used for measuring treatment effects. Compared to hazard ratio, its estimation procedure is robust against the non-proportional hazards assumption. We propose nonparametric Bayeisan (BNP) estimators for RMST using a dependent stick-breaking process prior mixture model that adjusts for mixed-type covariates. The proposed Bayesian estimators can yield both group-level causal estimate and subject-level predictions. Besides, we propose a novel dependent stick-breaking process prior that on average results in narrower credible intervals while maintaining similar coverage probability compared to a dependent probit stick-breaking process prior. We conduct simulation studies to investigate the performance of the proposed BNP RMST estimators compared to existing frequentist approaches and under different Bayesian modeling choices. The proposed framework is applied to estimate the treatment effect of an immuno therapy among KRAS wild-type colorectal cancer patients.

翻译：限制平均生存时间（RMST）是时间至事件随机变量的一种直观汇总统计量，可用于衡量治疗效果。与风险比相比，其估计过程对非比例风险假设具有稳健性。我们提出了一种贝叶斯非参数（BNP）估计器，用于估算RMST，该方法采用相依碎块断裂过程先验混合模型，并调整了混合类型协变量。所提出的贝叶斯估计器既能提供组级因果估计，也能提供个体水平预测。此外，我们提出了一种新颖的相依碎块断裂过程先验，与依赖 probit 碎块断裂过程先验相比，在保持相似覆盖概率的同时，平均而言产生更窄的置信区间。我们进行了模拟研究，以探究所提出的BNP RMST估计量与现有频率学派方法及不同贝叶斯建模选择相比的性能。该框架被应用于估计KRAS野生型结直肠癌患者中免疫疗法的治疗效果。