AI 中文总结
该研究针对含时间-事件结局的因果亚组估计问题，提出分层贝叶斯自助法框架，可稳定稀疏亚组的生存估计，模拟研究显示其性能优于贝叶斯加法回归树。
AI 中文摘要
在预设亚组（如生物标志物定义的层、疾病表型或人口统计组）内进行治疗效果的因果估计常具有临床意义。贝叶斯方法适用于亚组效应估计，因为灵活的先验可表征复杂的治疗异质性并传递后验不确定性。针对含时间-事件结局的预设因果亚组分析，也存在频率论方法，包括强调亚组层面协变量平衡的倾向得分加权方法。在后验g-公式中，亚组生存估计目标还依赖于亚组特定的基线协变量分布，当部分亚组规模较小或代表性不均时，该分布可能不稳定。右删失会单独降低估计目标的结局模型组分的信息，增加亚组因果生存对比的整体不确定性。我们将分层贝叶斯自助法（HBB）扩展到含右删失时间-事件结局的亚组因果推断中。HBB对亚组特定的基线协变量分布施加非参数分层先验，能在保留亚组特定结构的同时，合理地在相关亚组间借用信息。我们将该分布正则化与贝叶斯加速失效时间模型及右删失相结合，构建后验g-公式，该公式可传递来自生存模型和亚组协变量分布的双重不确定性。所得框架可在不对协变量分布施加参数假设的情况下，稳定稀疏层中的亚组因果生存估计目标。模拟研究检验了该方法在不同亚组稀疏度和删失程度下的性能，并将其与流行的贝叶斯加法回归树（用于异质性生存效应分析）进行了比较。
英文摘要
Causal estimation of treatment effects within prespecified subgroups, such as biomarker-defined strata, disease phenotypes, or demographic groups are often of clinical interest. Bayesian approaches are attractive for subgroup effect estimation because flexible priors can represent complex treatment heterogeneity and propagate posterior uncertainty. Frequentist methods for prespecified causal subgroup analysis with time-to-event outcomes are also available, including propensity-score weighting approaches that emphasize subgroup-level covariate balance. In the posterior g-formula, subgroup survival estimands also depend on the subgroup-specific distribution of baseline covariates, which may be unstable when some subgroups are small or unevenly represented. Right censoring separately reduces information for the outcome-model component of the estimand, increasing overall uncertainty in subgroup causal survival contrasts. We extend the hierarchical Bayesian bootstrap (HBB) to subgroup causal inference with right-censored time-to-event outcomes. The HBB places a nonparametric hierarchical prior on subgroup-specific baseline covariate distributions, enabling principled borrowing of information across related subgroups while preserving subgroup-specific structure. We combine this distributional regularization with a Bayesian accelerated failure time model and right censoring to perform a posterior g-formula that propagates uncertainty from both the survival model and the subgroup covariate distribution. The resulting framework stabilizes subgroup causal survival estimands in sparse strata without imposing parametric assumptions on the covariate distribution. Simulation studies examine performance across varying degrees of subgroup sparsity and censoring and compare the proposed approach to the popular Bayesian additive regression tree for heterogeneous survival effects.
