Doubly robust estimators for generalizing treatment effects on survival outcomes from randomized controlled trials to a target population
将随机试验的生存处理效应推广至目标总体的双重稳健估计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：摘要研究随机试验处理效应向目标总体的推广及双重稳健估计，直接对应因果推断。
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…
关注这篇论文的作者
将随机试验的生存处理效应推广至目标总体的双重稳健估计
- 研究问题
- 当随机试验参与者与目标总体存在异质性时，如何结合代表目标总体的观测样本，估计生存概率及限制平均生存时间的处理效应？
- 方法与思路
- 作者依据有效影响函数结合生存回归与抽样、删失及处理分配权重，构造半参数估计量，并用筛方法估计辅助函数。
- 材料陈述的贡献
- 摘要称估计量具有双重稳健性，两类模型均正确时局部有效；筛估计在相应条件下保持根号 n 一致性与有效性。
- 阅读关注点
- 应核对试验效应可推广的识别条件、删失处理、两类辅助模型的具体组成，以及筛估计的收敛率要求与方差构造。
- 阅读边界
- 这是 arXiv 预印本，同行评审与接收未核实。摘要未列出推广与收敛率的完整条件，模拟优势不能作为所有场景下的保证。
原文摘要
In the presence of heterogeneity between the randomized controlled trial (RCT) participants and the target population, evaluating the treatment effect solely based on the RCT often leads to biased quantification of the real-world treatment effect. To address the problem of lack of generalizability for the treatment effect estimated by the RCT sample, we leverage observational studies with large samples that are representative of the target population. This paper concerns evaluating treatment effects on survival outcomes for a target population and considers a broad class of estimands that are functionals of treatment-specific survival functions, including differences in survival probability and restricted mean survival times. Motivated by two intuitive but distinct approaches, i.e., imputation based on survival outcome regression and weighting based on inverse probability of sampling, censoring, and treatment assignment, we propose a semiparametric estimator through the guidance of the efficient influence function. The proposed estimator is doubly robust in the sense that it is consistent for the target population estimands if either the survival model or the weighting model is correctly specified, and is locally efficient when both are correct. In addition, as an alternative to parametric estimation, we employ the nonparametric method of sieves for flexible and robust estimation of the nuisance functions and show that the resulting estimator retains the root-n consistency and efficiency, the so-called it rate-double robustness. Simulation studies confirm the theoretical properties of the proposed estimator and show it outperforms competitors. We apply the proposed method to estimate the effect of adjuvant chemotherapy on survival in patients with early-stage resected non-small lung cancer.