Q-learning via deep learning-based Buckley-James method for non-linear censored data [0.03%]
基于深度学习的Buckley-James方法在非线性删失数据中的Q学习应用
Jeongjin Lee,Jong-Min Kim
Jeongjin Lee
In healthcare, personalized treatment strategies are vital for improving patient outcomes, especially under right-censored survival data. We propose Dynamic Deep Buckley-James Q-Learning, a novel counterfactual Q-learning algorithm that int...
Comparing adaptive treatment strategies in two-stage randomized trials with grouped survival data [0.03%]
基于分组生存数据的两阶段随机化试验中适应性治疗策略的比较
Monique Sparkman,Susan Halabi,Zhiguo Li
Monique Sparkman
Two-stage randomized trials, or the more general sequential multiple assignment randomized trials (SMART), have been increasingly used in studying adaptive treatment strategies for treating chronic diseases or conditions where treatments ne...
Comparative Study
Lifetime data analysis. 2026 Apr 2;32(2):26. DOI:10.1007/s10985-026-09708-y 2026
Nonparametric estimation of average effects of a continuous treatment for survival data with a cured fraction [0.03%]
连续治疗的生存数据中平均疗效的非参数估计（含治愈个体）
Hang Liu,Yingwei Peng
Hang Liu
Estimating the causal effect of a continuous treatment on survival data, particularly in cases where there is a cured fraction from observational studies, is a significant issue. However, this topic is not well addressed in the existing lit...
Estimating attributable risk functions for censored time-to-event in disease prevention research [0.03%]
疾病预防研究中估计截尾的事件发生时间的可归因风险函数
Ying Qing Chen,Yixin Wang,Xinyi Zhang et al.
Ying Qing Chen et al.
In disease prevention research, researchers often need to assess a prevention strategy that targets key disease-associated risk factors to reduce a population's disease burden. In this article, the fraction of the total disease burden assoc...
Partially linear Cox model with neural networks for left-truncated data [0.03%]
带有神经网络的偏线性Cox模型在左截断数据中的应用
Shiying Li,Li Shao,Shuwei Li
Shiying Li
In the past few decades, artificial neural networks (ANNs) have exhibited their superior capabilities for capturing nonlinear data patterns in supervised learning. Inspired by such desirable advantages, we herein explore the integration of ...
Nonparametric estimation of conditional survival function with time-varying covariates using DeepONet [0.03%]
基于DeepONet的时变协变量条件生存函数的非参数估计方法研究
Bingqing Hu,Bin Nan
Bingqing Hu
Traditional survival models often rely on restrictive assumptions such as proportional hazards or instantaneous effects of time-varying covariates on the hazard function, which limit their applicability in real-world settings. We consider t...
Nonparametric estimation of a state entry time distribution conditional on a "past" state occupation in a progressive multistate model with current status data [0.03%]
渐进性多状态模型中基于当前状态数据的“过去”状态停留时间条件下的状态进入时间分布的非参数估计
Samuel Anyaso-Samuel,Somnath Datta
Samuel Anyaso-Samuel
Inference for cause-specific cox model absolute risk in cohort subsampling designs [0.03%]
队列分层抽样设计下的特定原因COX模型的推断研究
Lola Etiévant,Mitchell H Gail
Lola Etiévant
The original case-cohort design obtains detailed covariate information on a random sample of subjects from the cohort (subcohort) and on the subjects who developed the event of interest (cases). Recently, there was some work on case-cohort ...
Semiparametric regression analysis of interval-censored competing risks data under additive hazards model with missing event types [0.03%]
具有缺失事件类型的区间删失竞争风险数据的加性危险模型半参数回归分析
Ruobing Jia,Yichen Lou,Jianguo Sun et al.
Ruobing Jia et al.
Interval-censored competing risks data frequently arise in medical and clinical studies among others and furthermore, the cause of failure may be missing in some situations. In this paper, we consider regression analysis of such data under ...
Doubly robust g-estimation of structural nested cumulative survival time models with non-ignorable, non-monotone missing data in time-varying confounders [0.03%]
非忽略、非单调缺失时间变化混杂变量的结构化嵌套累积生存时间模型的双重稳健g估计法
Yoshinori Takeuchi,Sho Komukai,Atsushi Goto et al.
Yoshinori Takeuchi et al.
To examine the causal effects of time-varying treatments on survival, structural nested cumulative survival time models (SNCSTMs) are flexible and theoretically promising semiparametric models characterized by causally interpretable paramet...