Lifetime Data Analysis最新文献

Bayesian generalized method of moments applied to pseudo-observations in survival analysis. 贝叶斯广义矩法在生存分析伪观测中的应用。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2025-09-22 DOI: 10.1007/s10985-025-09670-1

Léa Orsini, Caroline Brard, Emmanuel Lesaffre, Guosheng Yin, David Dejardin, Gwénaël Le Teuff

{"title":"Bayesian generalized method of moments applied to pseudo-observations in survival analysis.","authors":"Léa Orsini, Caroline Brard, Emmanuel Lesaffre, Guosheng Yin, David Dejardin, Gwénaël Le Teuff","doi":"10.1007/s10985-025-09670-1","DOIUrl":"https://doi.org/10.1007/s10985-025-09670-1","url":null,"abstract":"Bayesian inference for survival regression modeling offers numerous advantages, especially for decision-making and external data borrowing, but demands the specification of the baseline hazard function, which may be a challenging task. We propose an alternative approach that does not need the specification of this function. Our approach combines pseudo-observations to convert censored data into longitudinal data with the generalized method of moments (GMM) to estimate the parameters of interest from the survival function directly. GMM may be viewed as an extension of the generalized estimating equations (GEE) currently used for frequentist pseudo-observations analysis and can be extended to the Bayesian framework using a pseudo-likelihood function. We assessed the behavior of the frequentist and Bayesian GMM in the new context of analyzing pseudo-observations. We compared their performances to the Cox, GEE, and Bayesian piecewise exponential models through a simulation study of two-arm randomized clinical trials. Frequentist and Bayesian GMMs gave valid inferences with similar performances compared to the three benchmark methods, except for small sample sizes and high censoring rates. For illustration, three post-hoc efficacy analyses were performed on randomized clinical trials involving patients with Ewing Sarcoma, producing results similar to those of the benchmark methods. Through a simple application of estimating hazard ratios, these findings confirm the effectiveness of this new Bayesian approach based on pseudo-observations and the generalized method of moments. This offers new insights on using pseudo-observations for Bayesian survival analysis.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145114927","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

Pseudo-observations and super learner for the estimation of the restricted mean survival time. 估计有限平均生存时间的伪观察和超级学习器。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2025-09-22 DOI: 10.1007/s10985-025-09668-9

Ariane Cwiling, Vittorio Perduca, Olivier Bouaziz

引用次数: 0

Bayesian joint analysis of longitudinal data and interval-censored failure time data. 纵向数据和间隔截尾失效时间数据的贝叶斯联合分析。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2025-08-27 DOI: 10.1007/s10985-025-09666-x

Yuchen Mao, Lianming Wang, Xuemei Sui

{"title":"Bayesian joint analysis of longitudinal data and interval-censored failure time data.","authors":"Yuchen Mao, Lianming Wang, Xuemei Sui","doi":"10.1007/s10985-025-09666-x","DOIUrl":"10.1007/s10985-025-09666-x","url":null,"abstract":"Joint modeling of longitudinal responses and survival time has gained great attention in statistics literature over the last few decades. Most existing works focus on joint analysis of longitudinal data and right-censored data. In this article, we propose a new frailty model for joint analysis of a longitudinal response and interval-censored survival time. Such data commonly arise in real-life studies where participants are examined at periodical or irregular follow-up times. The proposed joint model contains a nonlinear mixed effects submodel for the longitudinal response and a semiparametric probit submodel for the survival time given a shared normal frailty. The proposed joint model allows the regression coefficients to be interpreted as the marginal effects up to a multiplicative constant on both the longitudinal and survival responses. Adopting splines allows us to approximate the unknown baseline functions in both submodels with only a finite number of unknown coefficients while providing great modeling flexibility. An efficient Gibbs sampler is developed for posterior computation, in which all parameters and latent variables can be sampled easily from their full conditional distributions. The proposed method shows a good estimation performance in simulation studies and is further illustrated by a real-life application to the patient data from the Aerobics Center Longitudinal Study. The R code for the proposed methodology is made available for public use.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2025-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144976304","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

Causal effect estimation on restricted mean survival time under case-cohort design via propensity score stratification. 通过倾向评分分层对病例队列设计下受限平均生存时间的因果效应估计。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2025-08-18 DOI: 10.1007/s10985-025-09667-w

Wei-En Lu, Ai Ni

引用次数: 0

Simultaneous clustering and joint modeling of multivariate binary longitudinal and time-to-event data. 多元二元纵向和时间-事件数据的同时聚类和联合建模。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-07-12 DOI: 10.1007/s10985-025-09664-z

Srijan Chattopadhyay, Sevantee Basu, Swapnaneel Bhattacharyya, Manash Pratim Gogoi, Kiranmoy Das

{"title":"Simultaneous clustering and joint modeling of multivariate binary longitudinal and time-to-event data.","authors":"Srijan Chattopadhyay, Sevantee Basu, Swapnaneel Bhattacharyya, Manash Pratim Gogoi, Kiranmoy Das","doi":"10.1007/s10985-025-09664-z","DOIUrl":"https://doi.org/10.1007/s10985-025-09664-z","url":null,"abstract":"Joint modeling of longitudinal outcomes and time-to-event data has been extensively used in medical studies because it can simultaneously model the longitudinal trajectories and assess their effects on the event-time. However, in many applications we come across heterogeneous populations, and therefore the subjects need to be clustered for a powerful statistical inference. We consider multivariate binary longitudinal outcomes for which we use Bayesian data-augmentation and get the corresponding latent continuous outcomes. These latent outcomes are clustered using Bayesian consensus clustering, and then we perform a cluster-specific joint analysis. Longitudinal outcomes are modeled by generalized linear mixed models, and we use the proportional hazards model for modeling time-to-event data. Our work is motivated by a clinical trial conducted by Tata Translational Cancer Research Center, Kolkata, where 184 cancer patients were treated for the first two years, and then were followed for the next three years. Three biomarkers (lymphocyte count, neutrophil count and platelet count), categorized as normal/abnormal, were measured during the treatment, and the relapse time (if any) was recorded for each patient. Our analysis finds three latent clusters for which the effects of the covariates and the median non-relapse probabilities substantially differ. Through a simulation study we illustrate the effectiveness of the proposed simultaneous clustering and joint modeling.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144621008","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

Analysis of interval censored survival data in sequential multiple assignment randomized trials. 序贯多任务随机试验中间隔截尾生存数据分析。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-07-11 DOI: 10.1007/s10985-025-09665-y

Zhiguo Li

引用次数: 0

Multi-source analyses of average treatment effects with failure time outcomes. 平均治疗效果与失效时间结果的多源分析。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-07-04 DOI: 10.1007/s10985-025-09663-0

Lan Wen, Jon A Steingrimsson, Sarah E Robertson, Issa J Dahabreh

{"title":"Multi-source analyses of average treatment effects with failure time outcomes.","authors":"Lan Wen, Jon A Steingrimsson, Sarah E Robertson, Issa J Dahabreh","doi":"10.1007/s10985-025-09663-0","DOIUrl":"https://doi.org/10.1007/s10985-025-09663-0","url":null,"abstract":"Analyses of multi-source data, such as data from multi-center randomized trials, individual participant data meta-analyses, or pooled analyses of observational studies, combine information to estimate an overall average treatment effect. However, if average treatment effects vary across data sources, commonly used approaches for multi-source analyses may not have a clear causal interpretation with respect to a target population of interest. In this paper, we provide identification and estimation of average treatment effects in a target population underlying one of the data sources in a point treatment setting for failure time outcomes potentially subject to right-censoring. We do not assume the absence of effect heterogeneity and hence our results are valid, under certain assumptions, when average treatment effects vary across data sources. We derive the efficient influence functions for source-specific average treatment effects using multi-source data under two different sets of assumptions, and propose a novel doubly robust estimator for our estimand. We evaluate the finite-sample performance of our estimator in simulation studies, and apply our methods to data from the HALT-C multi-center trials.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144561784","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

Shape-constrained estimation for current duration data in cross-sectional studies. 横断面研究中当前持续时间数据的形状约束估计。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2025-07-01 Epub Date: 2025-06-14 DOI: 10.1007/s10985-025-09658-x

Chi Wing Chu, Hok Kan Ling

引用次数: 0

Estimating the risk of cancer with and without a screening history. 评估有和没有筛查史的癌症风险。

IF 1 3区数学

Lifetime Data Analysis Pub Date : 2025-07-01 Epub Date: 2025-07-02 DOI: 10.1007/s10985-025-09662-1

Dongfeng Wu

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 3区数学

Lifetime Data Analysis Pub Date : 2025-07-01 Epub 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":"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":"473-497"},"PeriodicalIF":1.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12317910/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0