Lifetime Data Analysis最新文献

Instrumental variable estimation of a hazard ratio for treatment with a waiting time without specifying its dependence on unmeasured confounders: application to a procedural registry. 有等待时间的治疗风险比的工具变量估计，而不指定其对未测量混杂因素的依赖：应用于程序登记。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-04-29 DOI: 10.1007/s10985-026-09706-0

Todd A MacKenzie, Pablo Martínez-Camblor, Haobin Chen, Jesse A Columbo, A James O'Malley

引用次数: 0

A dependent and censored first hitting-time model with compound Poisson processes. 具有复合泊松过程的依赖和删节的首次撞击时间模型。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-04-27 DOI: 10.1007/s10985-026-09705-1

Mikael Escobar-Bach, Alexandre Popier, Malo Sahin

引用次数: 0

The semi-competing risk problem revisited. 半竞争风险问题再次出现。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-04-27 DOI: 10.1007/s10985-026-09709-x

Ross L Prentice, Aaron K Aragaki

引用次数: 0

Q-learning via deep learning-based Buckley-James method for non-linear censored data. 基于深度学习的非线性截尾数据的Buckley-James方法的q学习。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-04-06 DOI: 10.1007/s10985-026-09707-z

Jeongjin Lee, Jong-Min Kim

引用次数: 0

Comparing adaptive treatment strategies in two-stage randomized trials with grouped survival data. 比较两期随机试验与分组生存数据的适应性治疗策略。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-04-02 DOI: 10.1007/s10985-026-09708-y

Monique Sparkman, Susan Halabi, Zhiguo Li

{"title":"Comparing adaptive treatment strategies in two-stage randomized trials with grouped survival data.","authors":"Monique Sparkman, Susan Halabi, Zhiguo Li","doi":"10.1007/s10985-026-09708-y","DOIUrl":"10.1007/s10985-026-09708-y","url":null,"abstract":"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 need to be adjusted during the treatment course. Methods for analyzing two-stage randomized trials with continuous, longitudinal or right-censored survival endpoints have been developed. However, no method exists for data analysis for two-stage randomized trials with grouped survival endpoints, which are frequently encountered in practice. In this article, we propose methods for analyzing grouped survival data from two-stage randomized trials. We first extend the methods in Prentice and Gloeckler (1978) to allow for patient missing pre-specified visits, and use an efficient score test to make inferences on treatment effects with grouped data in traditional randomized trials. Based on this, we propose a weighted efficient score test to compare two adaptive treatment strategies with grouped survival data in two-stage randomized trials with missing visits. Asymptotic properties are formulated, and simulation studies are conducted to evaluate the performances of the proposed methods. We also apply the weighted score test in analyzing data from the sequenced treatment alternatives to relieve depression (STAR*D) trial.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13123648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonparametric estimation of average effects of a continuous treatment for survival data with a cured fraction. 对带有治愈分数的生存数据的连续治疗的平均效果的非参数估计。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-03-31 DOI: 10.1007/s10985-026-09703-3

Hang Liu, Yingwei Peng

{"title":"Nonparametric estimation of average effects of a continuous treatment for survival data with a cured fraction.","authors":"Hang Liu, Yingwei Peng","doi":"10.1007/s10985-026-09703-3","DOIUrl":"https://doi.org/10.1007/s10985-026-09703-3","url":null,"abstract":"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 literature. Current methods either rely on strong parametric assumptions or struggle to effectively control for confounding variables. In this study, we propose a novel nonparametric estimation method that utilizes a weighted generalized Kaplan-Meier survival estimator. This method aims to estimate the average effects of a continuous treatment on both the probability of being cured and the restricted mean survival time. Notably, our approach does not require any parametric assumptions about the effects, and it can efficiently control for multiple confounding variables. A simulation study demonstrates that our proposed method outperforms existing approaches, particularly when the average effects are complex or when confounding is strong. We apply this method to data from a study of chlamydia patients to evaluate the average effects of years of schooling on the probability of being immune to reinfection, as well as on the restricted mean survival time.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating attributable risk functions for censored time-to-event in disease prevention research. 估计疾病预防研究中审查时间到事件的归因风险函数。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-03-27 DOI: 10.1007/s10985-026-09704-2

Ying Qing Chen, Yixin Wang, Xinyi Zhang, Ross L Prentice

引用次数: 0

Nonparametric estimation of conditional survival function with time-varying covariates using DeepONet. 时变协变量条件生存函数的非参数估计。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-03-24 DOI: 10.1007/s10985-026-09700-6

Bingqing Hu, Bin Nan

{"title":"Nonparametric estimation of conditional survival function with time-varying covariates using DeepONet.","authors":"Bingqing Hu, Bin Nan","doi":"10.1007/s10985-026-09700-6","DOIUrl":"10.1007/s10985-026-09700-6","url":null,"abstract":"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 the nonparametric estimation of the conditional survival function, which leverages the flexibility of neural networks to capture the complex, potentially long-term non-instantaneous effects of time-varying covariates. In this work, we use Deep Operator Networks (DeepONet), a deep learning architecture designed for operator learning, to model the arbitrary effects of both time-varying and time-invariant covariates. Specifically, our method relaxes commonly used assumptions in hazard regressions by modeling the conditional hazard function as an unknown nonlinear operator of entire histories of time-varying covariates. The estimation is based on a loss function constructed from the nonparametric full likelihood for censored survival data. Simulation studies demonstrate that our method performs well, whereas the Cox model yields biased results when the assumption of instantaneous time-varying covariate effects is violated. We further illustrate its utility with the ADNI data, for which it yields a lower integrated Brier score than the Cox model.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13013183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516014","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonparametric estimation of a state entry time distribution conditional on a "past" state occupation in a progressive multistate model with current status data. 在具有当前状态数据的渐进多状态模型中，以“过去”状态职业为条件的状态进入时间分布的非参数估计

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-03-24 DOI: 10.1007/s10985-026-09699-w

Samuel Anyaso-Samuel, Somnath Datta

引用次数: 0

Partially linear Cox model with neural networks for left-truncated data. 左截断数据的神经网络部分线性Cox模型。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2026-03-24 DOI: 10.1007/s10985-026-09702-4

Shiying Li, Li Shao, Shuwei Li

{"title":"Partially linear Cox model with neural networks for left-truncated data.","authors":"Shiying Li, Li Shao, Shuwei Li","doi":"10.1007/s10985-026-09702-4","DOIUrl":"https://doi.org/10.1007/s10985-026-09702-4","url":null,"abstract":"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 ANNs with partially linear Cox model in the context of left truncation. This sampling scheme tends to enroll individuals with slower disease progression and thus leads to a biased sample of survival times in the target population. The proposed model comprises both parametric covariate effects and a nuisance function of uninterested covariates that is approximated with ANNs, offering a balance between interpretability and flexibility. We consider the conditional maximum likelihood estimation and derive a profile likelihood that is free of the baseline hazard function. An iterative algorithm embedded with stochastic gradient descent is proposed to minimize the negative profile log-likelihood, leading to the estimators of the regression parameters and ANNs simultaneously. Extensive simulation studies and an application demonstrate that the proposed method outperforms the traditional approaches regarding the estimation accuracy and predictive capability.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.0,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516132","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0