Lifetime Data Analysis最新文献

Investigating network structures in recurrent event data with discrete observation times. 研究具有离散观测时间的循环事件数据中的网络结构。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-05-23 DOI: 10.1007/s10985-025-09656-z

Yufeng Xia, Yangkuo Li, Xiaobing Zhao, Xuan Xu

引用次数: 0

Regression analysis of a graphical proportional hazards model for informatively left-truncated current status data. 信息左截断当前状态数据的图形比例风险模型的回归分析。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-05-10 DOI: 10.1007/s10985-025-09655-0

Mengyue Zhang, Shishu Zhao, Shuying Wang, Xiaolin Xu

{"title":"Regression analysis of a graphical proportional hazards model for informatively left-truncated current status data.","authors":"Mengyue Zhang, Shishu Zhao, Shuying Wang, Xiaolin Xu","doi":"10.1007/s10985-025-09655-0","DOIUrl":"https://doi.org/10.1007/s10985-025-09655-0","url":null,"abstract":"In survival analysis, researchers commonly focus on variable selection issues in real-world data, particularly when complex network structures exist among covariates. Additionally, due to factors such as data collection costs and delayed entry, real-world data often exhibit censoring and truncation phenomena.This paper addresses left-truncated current status data by employing a copula-based approach to model the relationship between censoring time and failure time. Based on this, we investigate the problem of variable selection in the context of complex network structures among covariates. To this end, we integrate Markov Random Field (MRF) with the Proportional Hazards (PH) model, and extend the latter to more flexibly characterize the correlation structure among covariates. For solving the constructed model, we propose a penalized optimization method and utilize spline functions to estimate the baseline hazard function. Through numerical simulation experiments and case studies of clinical trial data, we comprehensively evaluate the effectiveness and performance of the proposed model and its parameter inference strategy. This evaluation not only demonstrates the robustness of the proposed model in handling complex disease data but also further verifies the high precision and reliability of the parameter estimation method.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144047308","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

Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding. 高维RCT与RWD的综合分析。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-29 DOI: 10.1007/s10985-025-09654-1

Xin Ye, Shu Yang, Xiaofei Wang, Yanyan Liu

{"title":"Integrative analysis of high-dimensional RCT and RWD subject to censoring and hidden confounding.","authors":"Xin Ye, Shu Yang, Xiaofei Wang, Yanyan Liu","doi":"10.1007/s10985-025-09654-1","DOIUrl":"https://doi.org/10.1007/s10985-025-09654-1","url":null,"abstract":"In this study, we focus on estimating the heterogeneous treatment effect (HTE) for survival outcome. The outcome is subject to censoring and the number of covariates is high-dimensional. We utilize data from both the randomized controlled trial (RCT), considered as the gold standard, and real-world data (RWD), possibly affected by hidden confounding factors. To achieve a more efficient HTE estimate, such integrative analysis requires great insight into the data generation mechanism, particularly the accurate characterization of unmeasured confounding effects/bias. With this aim, we propose a penalized-regression-based integrative approach that allows for the simultaneous estimation of parameters, selection of variables, and identification of the existence of unmeasured confounding effects. The consistency, asymptotic normality, and efficiency gains are rigorously established for the proposed estimate. Finally, we apply the proposed method to estimate the HTE of lobar/sublobar resection on the survival of lung cancer patients. The RCT is a multicenter non-inferiority randomized phase 3 trial, and the RWD comes from a clinical oncology cancer registry in the United States. The analysis reveals that the unmeasured confounding exists and the integrative approach does enhance the efficiency for the HTE estimation.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144003384","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

Quantile regression under dependent censoring with unknown association. 关联未知的相关审查下的分位数回归。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-03-16 DOI: 10.1007/s10985-025-09647-0

Myrthe D'Haen, Ingrid Van Keilegom, Anneleen Verhasselt

{"title":"Quantile regression under dependent censoring with unknown association.","authors":"Myrthe D'Haen, Ingrid Van Keilegom, Anneleen Verhasselt","doi":"10.1007/s10985-025-09647-0","DOIUrl":"10.1007/s10985-025-09647-0","url":null,"abstract":"The study of survival data often requires taking proper care of the censoring mechanism that prohibits complete observation of the data. Under right censoring, only the first occurring event is observed: either the event of interest, or a competing event like withdrawal of a subject from the study. The corresponding identifiability difficulties led many authors to imposing (conditional) independence or a fully known dependence between survival and censoring times, both of which are not always realistic. However, recent results in survival literature showed that parametric copula models allow identification of all model parameters, including the association parameter, under appropriately chosen marginal distributions. The present paper is the first one to apply such models in a quantile regression context, hence benefiting from its well-known advantages in terms of e.g. robustness and richer inference results. The parametric copula is supplemented with a likewise parametric, yet flexible, enriched asymmetric Laplace distribution for the survival times conditional on the covariates. Its asymmetric Laplace basis provides its close connection to quantiles, while the extension with Laguerre orthogonal polynomials ensures sufficient flexibility for increasing polynomial degrees. The distributional flavour of the quantile regression presented, comes with advantages of both theoretical and computational nature. All model parameters are proven to be identifiable, consistent, and asymptotically normal. Finally, performance of the model and of the proposed estimation procedure is assessed through extensive simulation studies as well as an application on liver transplant data.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"253-299"},"PeriodicalIF":1.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639770","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

Lifetime analysis with monotonic degradation: a boosted first hitting time model based on a homogeneous gamma process. 具有单调退化的寿命分析：一种基于均匀伽马过程的改进的首次撞击时间模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-04-05 DOI: 10.1007/s10985-025-09648-z

Clara Bertinelli Salucci, Azzeddine Bakdi, Ingrid Kristine Glad, Bo Henry Lindqvist, Erik Vanem, Riccardo De Bin

引用次数: 0

Total time on test-based goodness-of-fit statistics for the reciprocal property in fatigue-life models. 疲劳寿命模型中互反特性的基于试验的拟合优度统计的总时间。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-04-26 DOI: 10.1007/s10985-025-09653-2

Cecilia Castro, Marta Azevedo, Víctor Leiva, Luís Meira-Machado

{"title":"Total time on test-based goodness-of-fit statistics for the reciprocal property in fatigue-life models.","authors":"Cecilia Castro, Marta Azevedo, Víctor Leiva, Luís Meira-Machado","doi":"10.1007/s10985-025-09653-2","DOIUrl":"https://doi.org/10.1007/s10985-025-09653-2","url":null,"abstract":"We propose a new goodness-of-fit procedure designed to verify the reciprocal property that characterizes the fatigue-life or Birnbaum-Saunders (BS) distribution. Under this property, scaling a random variable that takes positive values by its median results in the same distribution as its reciprocal, a feature frequently encountered in reliability and survival studies. Our procedure employs total time on test (TTT) curves to compare the behavior of the observed data and its reciprocal counterpart, capturing both local and global discrepancies through supremum- and area-based statistics. We establish the theoretical validity of these statistics under mild assumptions, showing that they deliver accurate inference for moderate to large samples. Simulation evidence indicates that our TTT-based procedures are sensitive to subtle departures from log-symmetry, particularly when the distribution underlying the data has heavier or lighter tails than the assumed one. Illustrative real data examples further reveal how overlooking deviations from the reciprocal property can distort reliability estimates and predictions of failure times, showing the practical importance of the new goodness-of-fit procedure. Overall, our findings strengthen the BS framework and provide robust tools for model validation and selection when log-symmetric modeling assumptions are in place.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"31 2","pages":"422-441"},"PeriodicalIF":1.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054576","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

A pairwise pseudo-likelihood approach for regression analysis of doubly truncated data. 双截断数据回归分析的两两伪似然方法。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-03-31 DOI: 10.1007/s10985-025-09649-y

Cunjin Zhao, Peijie Wang, Jianguo Sun

引用次数: 0

Goodness-of-fit testing in the presence of cured data: IPCW approach. 存在固化数据的拟合优度检验：IPCW方法。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-03-04 DOI: 10.1007/s10985-025-09646-1

Marija Cuparić, Bojana Milošević

引用次数: 0

A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding. 一个灵活的贝叶斯g公式的因果生存分析与时间相关的混淆。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-04-14 DOI: 10.1007/s10985-025-09652-3

Xinyuan Chen, Liangyuan Hu, Fan Li

引用次数: 0

Robust inverse probability weighted estimators for doubly truncated Cox regression with closed-form standard errors. 具有封闭标准误差的双截断Cox回归的鲁棒逆概率加权估计。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-04-15 DOI: 10.1007/s10985-025-09650-5

Omar Vazquez, Sharon X Xie

{"title":"Robust inverse probability weighted estimators for doubly truncated Cox regression with closed-form standard errors.","authors":"Omar Vazquez, Sharon X Xie","doi":"10.1007/s10985-025-09650-5","DOIUrl":"https://doi.org/10.1007/s10985-025-09650-5","url":null,"abstract":"Survival data is doubly truncated when only participants who experience an event during a random interval are included in the sample. Existing methods typically correct for double truncation bias in Cox regression through inverse probability weighting via the nonparametric maximum likelihood estimate (NPMLE) of the selection probabilities. This approach relies on two key assumptions, quasi-independent truncation and positivity of the sampling probabilities, yet there are no methods available to thoroughly assess these assumptions in the regression context. Furthermore, these estimators can be particularly sensitive to extreme event times. Finally, current double truncation methods rely on bootstrapping for variance estimation. Aside from the unnecessary computational burden, there are often identifiability issues with the NPMLE during bootstrap resampling. To address these limitations of current methods, we propose a class of robust Cox regression coefficient estimators with time-varying inverse probability weights and extend these estimators to conduct sensitivity analysis regarding possible non-positivity of the sampling probabilities. Also, we develop a nonparametric test and graphical diagnostic for verifying the quasi-independent truncation assumption. Finally, we provide closed-form standard errors for the NPMLE as well as for the proposed estimators. The proposed estimators are evaluated through extensive simulations and illustrated using an AIDS study.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"31 2","pages":"364-393"},"PeriodicalIF":1.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12043752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049810","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