Lifetime Data Analysis最新文献_第4页

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 : 2025-10-01 Epub 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":"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":"970-993"},"PeriodicalIF":1.0,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12586244/pdf/","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":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub 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":"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":"869-897"},"PeriodicalIF":1.0,"publicationDate":"2025-10-01","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

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 3区数学

Lifetime Data Analysis Pub Date : 2025-10-01 Epub 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":"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":"830-851"},"PeriodicalIF":1.0,"publicationDate":"2025-10-01","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

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 : 2025-10-01 Epub Date: 2025-09-22 DOI: 10.1007/s10985-025-09668-9

Ariane Cwiling, Vittorio Perduca, Olivier Bouaziz

引用次数: 0

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 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 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

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 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":1.0,"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

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 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":1.0,"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

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 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

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 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":1.0,"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