Lifetime Data Analysis最新文献_第7页

A comparison of Kaplan-Meier-based inverse probability of censoring weighted regression methods. 基于kaplan - meier逆概率滤波加权回归方法的比较。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-10-28 DOI: 10.1007/s10985-025-09669-8

Morten Overgaard

引用次数: 0

Modelling dependent censoring in time-to-event data using boosting copula regression. 基于增强联结回归的时间-事件数据相关滤波建模。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-10-21 DOI: 10.1007/s10985-025-09674-x

Annika Strömer, Nadja Klein, Ingrid Van Keilegom, Andreas Mayr

{"title":"Modelling dependent censoring in time-to-event data using boosting copula regression.","authors":"Annika Strömer, Nadja Klein, Ingrid Van Keilegom, Andreas Mayr","doi":"10.1007/s10985-025-09674-x","DOIUrl":"10.1007/s10985-025-09674-x","url":null,"abstract":"Survival analysis plays a pivotal role across disciplines, including engineering, economics, and social sciences—not just in biomedical research. In many of these applications, incomplete observations due to censoring are common, arising from limited follow-up periods, study dropouts, or administrative constraints. A standard assumption in such settings is that the censoring mechanism is independent of the survival process. This assumption primarily holds when censoring occurs at the end of the observation period. However, there may be dependence between event and censoring times. For example, if a patient’s health deteriorates and they withdraw due to poor prognosis, the time of censoring depends on their health status, leading to dependent censoring as sicker patients are censored earlier. To address such situations adequately in statistical analyses, we propose a model-based boosting approach using distributional copula regression. Our approach models the joint distribution of survival and censoring times by linking unknown marginal distributions through an unknown parametric copula. All distribution parameters of the resulting joint distribution are estimated simultaneously as functions of potentially different covariates. A key merit of the boosting approach is its data-driven variable selection, which is particularly important for such flexible models. Estimation remains feasible even for high-dimensional data with more covariates than observations, where classical estimation frameworks meet their limits. To investigate the performance of our method, we conduct a comprehensive simulation study, and demonstrate its practical application using a recent observational study analyzing the overall survival of patients with colon cancer. The data has a high proportion of right-censored observations without information on the cause of censoring.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"994-1016"},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12586418/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145338136","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

Statistical methods for composite analysis of recurrent and terminal events in clinical trials. 临床试验中复发性和终末期事件综合分析的统计方法。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-10-15 DOI: 10.1007/s10985-025-09672-z

Yiyuan Huang, Douglas Schaubel, Min Zhang

{"title":"Statistical methods for composite analysis of recurrent and terminal events in clinical trials.","authors":"Yiyuan Huang, Douglas Schaubel, Min Zhang","doi":"10.1007/s10985-025-09672-z","DOIUrl":"10.1007/s10985-025-09672-z","url":null,"abstract":"In many clinical trials, one is interested in evaluating the treatment effect based on different types of outcomes, including recurrent and terminal events. The most popular approach is the time-to-first-event analysis (TTFE), based on the composite outcome of the time to the first event among all events of interest. The motivation for the composite outcome approach is to increase the number of events and potentially increase power. Other composite outcome or composite analysis methods are also studied in the literature, but are less adopted in practice. In this article, we first review the mainstream composite analysis methods and classify them into three categories: (A) Composite-outcome Methods, which combine multiple events into a composite outcome before analysis, e.g., combining events into a time-to-event outcome in TTFE and into a single recurrent event process in the combined-recurrent-event analysis (CRE); (B) Joint-analysis Methods, which test for the recurrent event process and the terminal event jointly, e.g., Joint Frailty Model (JFM), Ghosh-Lin Method (GL), and Nelsen-Aalen Method (NA); (C) Win-ratio type Methods that account for the ordering of two types of events, e.g., Win-fraction Regression (WR). We conduct comprehensive simulation studies to evaluate the performance of various types of methods in terms of type I error control and power under a wide range of scenarios. We found that the non-parametric joint testing approach (GL/NA) and CRE have overall the best performance. However, TTFE and WR exhibit relatively low power. Also, adding events that have no or weak association with treatment usually decreases power.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"810-829"},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145294258","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 1.5 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-07-11 DOI: 10.1007/s10985-025-09665-y

Zhiguo Li

引用次数: 0

Assessing delayed treatment benefits of immunotherapy using long-term average hazard: a novel test/estimation approach. 使用长期平均危害评估免疫治疗的延迟治疗益处：一种新的测试/估计方法。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-10-14 DOI: 10.1007/s10985-025-09671-0

Miki Horiguchi, Lu Tian, Kenneth L Kehl, Hajime Uno

{"title":"Assessing delayed treatment benefits of immunotherapy using long-term average hazard: a novel test/estimation approach.","authors":"Miki Horiguchi, Lu Tian, Kenneth L Kehl, Hajime Uno","doi":"10.1007/s10985-025-09671-0","DOIUrl":"10.1007/s10985-025-09671-0","url":null,"abstract":"Delayed treatment effects on time-to-event outcomes are commonly observed in randomized controlled trials of cancer immunotherapies. When the treatment effect has a delayed onset, the conventional test/estimation approach-using the log-rank test for between-group comparison and Cox's hazard ratio to quantify the treatment effect-can be suboptimal. The log-rank test may lack power in such scenarios, and the interpretation of the hazard ratio is often ambiguous. Recently, alternative test/estimation approaches have been proposed to address these limitations. One such approach is based on long-term restricted mean survival time (LT-RMST), while another is based on average hazard with survival weight (AH-SW). This paper integrates these two concepts and introduces a novel long-term average hazard (LT-AH) approach with survival weight for both hypothesis testing and estimation. Numerical studies highlight specific scenarios where the proposed LT-AH method achieves higher power than the existing alternatives. The LT-AH for each group can be estimated nonparametrically, and the proposed between-group comparison maintains test/estimation coherency. Because the difference and ratio of LT-AH do not rely on model assumptions about the relationship between two groups, the LT-AH approach provides a robust framework for estimating the magnitude of between-group differences. Furthermore, LT-AH allows for treatment effect quantification in both absolute (difference in LT-AH) and relative (ratio of LT-AH) terms, aligning with guideline recommendations and addressing practical needs. Given its interpretability and improved power in certain settings, the proposed LT-AH approach offers a useful alternative to conventional hazard-based methods, particularly when delayed treatment effects are expected.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"784-809"},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12586407/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145287554","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

Bayesian joint models for longitudinal, recurrent, and terminal event data. 纵向、循环和终端事件数据的贝叶斯联合模型。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-10-09 DOI: 10.1007/s10985-025-09673-y

Emily M Damone, Matthew A Psioda, Joseph G Ibrahim

{"title":"Bayesian joint models for longitudinal, recurrent, and terminal event data.","authors":"Emily M Damone, Matthew A Psioda, Joseph G Ibrahim","doi":"10.1007/s10985-025-09673-y","DOIUrl":"10.1007/s10985-025-09673-y","url":null,"abstract":"Many methods exist to jointly model either recurrent and related terminal survival events or longitudinal outcome measures and related terminal survival event. However, few methods exist which can account for the dependency between all three outcomes of interest, and none allow for the modeling of all three outcomes without strong correlation assumptions. We propose a joint model which uses subject-specific random effects to connect the survival model (terminal and recurrent events) with a longitudinal outcome model. In the proposed method, proportional hazards models with shared frailties are used to model dependence between the recurrent and terminal events, while a separate (but correlated) set of random effects are utilized in a generalized linear mixed model to model dependence with longitudinal outcome measures. All random effects are related based on an assumed multivariate normal distribution. The proposed joint modeling approach allows for flexible models, particularly for unique longitudinal trajectories, that can be utilized in a wide range of health applications. We evaluate the model through simulation studies as well as through an application to data from the Atherosclerosis Risk in Communities (ARIC) study.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"932-949"},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145253405","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 1.5 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub Date: 2025-08-18 DOI: 10.1007/s10985-025-09667-w

Wei-En Lu, Ai Ni

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 1.5 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub 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":"950-969"},"PeriodicalIF":1.5,"publicationDate":"2025-10-01","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

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 1.5 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 1.5 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