Bayesian hierarchical bootstrap framework for causal subgroup estimation with a time-to-event outcome
事件时间结果因果亚组估计的贝叶斯层级自助框架AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要直接研究预设亚组内处理效应及右删失生存结果的因果估计。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
事件时间结果因果亚组估计的贝叶斯层级自助框架
- 研究问题
- 预先指定的亚组样本稀疏且生存结果右删失时，如何稳定估计亚组因果生存差异，并传播协变量分布与生存模型的不确定性？
- 方法与思路
- 作者对亚组基线协变量分布设置层级贝叶斯自助先验，结合贝叶斯加速失效时间模型，通过后验 g 公式计算生存目标量。
- 材料陈述的贡献
- 摘要称该框架通过亚组间信息借用稳定稀疏亚组估计，无须参数化协变量分布，并模拟比较不同稀疏及删失情景。
- 阅读关注点
- 应核对亚组间信息借用的先验强度、生存因果目标及删失处理条件，以及协变量分布与结果模型的不确定性如何进入后验。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未经核验；摘要未说明因果识别假设、先验敏感性及比较数值，无法确认普遍改善。
原文摘要
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.