Lifetime Data Analysis最新文献

Estimation and variable selection for semiparametric transformation models with length-biased survival data. 具有长度偏倚生存数据的半参数转换模型的估计和变量选择。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-07-16 DOI: 10.1007/s10985-025-09661-2

Jih-Chang Yu, Yu-Jen Cheng

{"title":"Estimation and variable selection for semiparametric transformation models with length-biased survival data.","authors":"Jih-Chang Yu, Yu-Jen Cheng","doi":"10.1007/s10985-025-09661-2","DOIUrl":"https://doi.org/10.1007/s10985-025-09661-2","url":null,"abstract":"In this study, we investigate estimation and variable selection for semiparametric transformation models with length-biased survival data-a special case of left truncation commonly encountered in the social sciences and cancer prevention trials. To correct for sampling bias, conventional methods such as conditional likelihood, martingale estimating equations, and composite likelihood have been proposed. However, these methods may be less efficient due to their reliance on only partial information from the full likelihood. In contrast, we adopt a full-likelihood approach under the semiparametric transformation model and propose a unified and more efficient nonparametric maximum likelihood estimator (NPMLE). To perform variable selection, we incorporate an adaptive least absolute shrinkage and selection operator (ALASSO) penalty into the full likelihood. We show that when the NPMLE is used as the initial value, the resulting one-step ALASSO estimator-offering a simplified version of the Newton-Raphson method-achieves oracle properties. Theoretical properties of the proposed methods are established using empirical process techniques. The performance of the methods is evaluated through simulation studies and illustrated with a real data application.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144643970","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

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

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 1.2 3区数学

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

Dongfeng Wu

引用次数: 0

Bayesian bivariate cure rate models using Gaussian copulas. 基于高斯copuls的贝叶斯二元治愈率模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-06-25 DOI: 10.1007/s10985-025-09660-3

Seoyoon Cho, Matthew A Psioda, Joseph G Ibrahim

{"title":"Bayesian bivariate cure rate models using Gaussian copulas.","authors":"Seoyoon Cho, Matthew A Psioda, Joseph G Ibrahim","doi":"10.1007/s10985-025-09660-3","DOIUrl":"https://doi.org/10.1007/s10985-025-09660-3","url":null,"abstract":"We propose a joint model for multiple time-to-event outcomes where the outcomes have a cure structure. When a subset of a population is not susceptible to an event of interest, traditional survival models cannot accommodate this type of phenomenon. For example, for patients with melanoma, certain modern treatment options can reduce the mortality and relapse rates. Traditional survival models assume the entire population is at risk for the event of interest, i.e., has a non-zero hazard at all times. However, cure rate models allow a portion of the population to be risk-free of the event of interest. Our proposed model uses a novel truncated Gaussian copula to jointly model bivariate time-to-event outcomes of this type. In oncology studies, multiple time-to-event outcomes (e.g., overall survival and relapse-free or progression-free survival) are typically of interest. Therefore, multivariate methods to analyze time-to-event outcomes with a cure structure are potentially of great utility. We formulate a joint model directly on the time-to-event outcomes (i.e., unconditional on whether an individual is cured or not). Dependency between the time-to-event outcomes is modeled via the correlation matrix of the truncated Gaussian copula. A Markov Chain Monte Carlo procedure is proposed for model fitting. Simulation studies and a real data analysis using a melanoma clinical trial data are presented to illustrate the performance of the method and the proposed model is compared to independent models.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144486712","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 1.2 3区数学

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

Chi Wing Chu, Hok Kan Ling

引用次数: 0

Design and analysis of individually randomized group-treatment trials with time to event outcomes. 随机分组治疗试验的设计和分析。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-06-03 DOI: 10.1007/s10985-025-09657-y

Sin-Ho Jung

{"title":"Design and analysis of individually randomized group-treatment trials with time to event outcomes.","authors":"Sin-Ho Jung","doi":"10.1007/s10985-025-09657-y","DOIUrl":"https://doi.org/10.1007/s10985-025-09657-y","url":null,"abstract":"In a typical individually randomized group-treatment (IRGT) trial, subjects are randomized between a control arm and an experimental arm. While the subjects randomized to the control arm are treated individually, those in the experimental arm are assigned to one of clusters for group treatment. By sharing some common frailties, the outcomes of subjects in the same groups tend to be dependent, whereas those in the control arm are independent. In this paper, we consider IRGT trials with time to event outcomes. We modify the two-sample log-rank test to compare the survival data from TRGT trials, and derive its sample size formula. The proposed sample size formula requires specification of marginal survival distributions for the two arms, bivariate survival distribution and cluster size distribution for the experimental arm, and accrual period or accrual rate together with additional follow-up period. In a sample size calculation, either the cluster sizes are given and the number of clusters is calculated or the number of clusters is given at the time of study open and the required accrual period to determine the cluster sizes is calculated. Simulations and a real data example show that the proposed test statistic controls the type I error rate and the formula provides accurately powered sample sizes. Also proposed are optimal designs minimizing the total sample size or the total cost when the cost per subject is different between two treatment arms.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":""},"PeriodicalIF":1.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144217400","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

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