Lifetime Data Analysis最新文献_第3页

Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia. 基于反射布朗运动对复发性低血糖的撞击时间的阈值回归中未观察到的异质性。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-06-02 DOI: 10.1007/s10985-026-09717-x

Yingfa Xie, Haoda Fu, Yuan Huang, Jun Yan

{"title":"Unobserved heterogeneity in threshold regression based on the hitting times of a reflected Brownian motion for recurrent hypoglycemia.","authors":"Yingfa Xie, Haoda Fu, Yuan Huang, Jun Yan","doi":"10.1007/s10985-026-09717-x","DOIUrl":"10.1007/s10985-026-09717-x","url":null,"abstract":"Analyses of recurrent hypoglycemia are critical for effective treatment management in diabetic patients. Typically, within-subject dependency in such analyses is captured through subject-level frailty. Recent research has modeled recurrent hypoglycemia using the first hitting times of a reflected Brownian motion. A close examination of this approach reveals that it does not adequately account for varying frailties among individuals, which indicate notable heterogeneity. To address this gap, we propose a finite mixture model of the first hitting time distribution of the reflected Brownian motion. This model allows for component-specific regression coefficients and frailty parameters, providing nuanced insights into how risk factors differently affect patient subgroups. We employ a Bayesian framework for inference, utilizing Markov chain Monte Carlo for estimation. Model selection is conducted using the Deviance Information Criterion and the Logarithm of the Pseudo-Marginal Likelihood. The effectiveness of these criteria is assessed through simulation studies. Application to recurrent hypoglycemia modeling revealed two subgroups with different risk profiles, as reflected in their volatilities. Bayesian model comparison criteria favor the model with component-specific regression coefficients for volatilities. The subgroup with lower volatility exhibits a larger variance and, hence, a greater level of heterogeneity.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148145269","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

Variable selection with broken adaptive ridge regression for interval-censored competing risks data. 区间剔除竞争风险数据的破碎自适应脊回归变量选择。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-05-29 DOI: 10.1007/s10985-026-09711-3

Fatemeh Mahmoudi, Chenxi Li, Kaida Cai, Xuewen Lu

引用次数: 0

Training-set conditionally valid prediction sets with right-censored data. 具有右截尾数据的训练集条件有效预测集。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-05-21 DOI: 10.1007/s10985-026-09697-y

Wenwen Si, Hongxiang Qiu

{"title":"Training-set conditionally valid prediction sets with right-censored data.","authors":"Wenwen Si, Hongxiang Qiu","doi":"10.1007/s10985-026-09697-y","DOIUrl":"10.1007/s10985-026-09697-y","url":null,"abstract":"Uncertainty quantification of prediction models through prediction sets is increasingly popular and successful, but most existing methods rely on directly observing the outcome and do not appropriately handle censored outcomes, such as time-to-event outcomes. Candès et al. (2023) and Gui et al. (2024) have introduced distribution-free conformal prediction methods that construct predictive intervals for right-censored outcomes with marginal coverage guarantees. However, these methods typically assume a restrictive Type I censoring framework, in which censoring times are all observed. In this paper, we leverage a semiparametric one-step estimation framework and propose a novel approach for constructing predictive lower bounds on survival times with training-set conditional validity under right-censoring, where censoring times may be unobserved when the survival time is observed. With slight modification, our method can also provide predictive lower bounds with marginal guarantees. Through extensive simulations and a real-world application dataset tracking users' active times on a mobile application, we demonstrate the effectiveness and practicality of our approach. Compared to existing methods, our technique shows superior efficiency and robustness to model misspecifications, representing a significant advancement in the toolbox for reliable machine learning for time-to-event outcomes.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13194291/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147984142","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

Kolmogorov-Smirnov and Cramér-von Mises tests for the k-sample problem with left-truncated and right-censored data. Kolmogorov-Smirnov和cramsamr -von Mises对左截和右截数据的k样本问题的检验。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-05-16 DOI: 10.1007/s10985-026-09713-1

Adrián Lago, Juan Carlos Pardo-Fernández, Jacobo de Uña-Álvarez

引用次数: 0

Gradient boosting-based discrete failure time model for selecting time-varying effects and interactions. 基于梯度升压的时变效应和相互作用选择的离散失效时间模型。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-05-13 DOI: 10.1007/s10985-026-09710-4

Lingfeng Luo, Kevin He, Jeremy M G Taylor

{"title":"Gradient boosting-based discrete failure time model for selecting time-varying effects and interactions.","authors":"Lingfeng Luo, Kevin He, Jeremy M G Taylor","doi":"10.1007/s10985-026-09710-4","DOIUrl":"10.1007/s10985-026-09710-4","url":null,"abstract":"Analyzing survival data from the National Cancer Institute's Surveillance, Epidemiology, and End Results (SEER) Program offers invaluable insight to guide cancer management. In these data the event times are recorded in units of months. In such datasets, it can be important to consider the selection of covariates, distinguish between time-varying and time-independent effects, and capture important interaction terms. However, variable selection is difficult to combine with the differentiation between time-varying and time-independent effects. Moreover, accurately selecting interaction terms is more challenging because of the hierarchy restriction that an interaction term should only enter a model if one or both associated main effects are also included. To resolve these issues, we develop a coordinate ascent-based gradient boosting procedure for discrete failure time models. This approach achieves variable selection in high-dimensional settings, differentiates between time-varying and time-independent variables, and selects important interaction terms under hierarchy restrictions. Traditional boosting stopping criteria are ad hoc, while preferable information-criteria-based rules necessitate defining degrees of freedom, a challenge in survival models. Our approach provides well-defined degrees of freedom and offers information-criteria-based stopping rules. Simulation studies show the proposed method achieves good selection performance. We apply the proposed method to SEER melanoma cancer data and select important risk factors, distinguish between time-varying and time-dependent effects and detect important two-way interactions.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13167897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147935000","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

Reduction techniques for survival analysis. 生存分析的还原技术。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-05-10 DOI: 10.1007/s10985-026-09714-0

Johannes Piller, Léa Orsini, Simon Wiegrebe, Sophie Hanna Langbein, Lukas Burk, John Zobolas, Philip Studener, Markus Goeswein, Andreas Bender

引用次数: 0

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 1.5 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

{"title":"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.","authors":"Todd A MacKenzie, Pablo Martínez-Camblor, Haobin Chen, Jesse A Columbo, A James O'Malley","doi":"10.1007/s10985-026-09706-0","DOIUrl":"10.1007/s10985-026-09706-0","url":null,"abstract":"The timing of an intervention such as surgery may happen sometime after treatment inception (e.g., diagnosis date) due to being put on a wait-list for treatment or being scheduled for treatment at a later time. If interest lies in the change in risk upon receiving the intervention, a Cox model with a time-dependent exposure may be employed. However, if there exists unmeasured confounding, maximum partial likelihood estimators of the hazard ratio are biased. An instrumental variable is a cause of the exposure of interest but not of the outcome except through the exposure. We propose a new framework involving potential outcomes for each possible waiting time, and derive an estimating equation for the hazard ratio of a treatment with a time-dependent start using an instrumental variable without making any assumptions about the form of involvement of unmeasured confounders. We conducted simulations to evaluate bias of our hazard ratio estimator. We illustrate the approach using two examples: First, we analyzed a procedural registry for which instrumental variable based estimators may be the only unbiased approach. Second, we demonstrate causal hazard ratio estimation for a randomized trial with time-dependent treatment initiation.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13128713/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147787147","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

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 1.5 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 1.5 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。