Lifetime Data Analysis最新文献_第5页

Robust functional Cox regression model. 稳健功能Cox回归模型。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-02-23 DOI: 10.1007/s10985-026-09694-1

Gizel Bakicierler Sezer, Ufuk Beyaztas

{"title":"Robust functional Cox regression model.","authors":"Gizel Bakicierler Sezer, Ufuk Beyaztas","doi":"10.1007/s10985-026-09694-1","DOIUrl":"10.1007/s10985-026-09694-1","url":null,"abstract":"Survival analysis with functional covariates has emerged as an important extension of the classical Cox proportional hazards model, allowing one to assess how entire trajectories or curves influence time-to-event outcomes. However, existing functional Cox models are typically fitted using non-robust techniques and can be highly sensitive to outliers or aberrant observations in the data. In this paper, we propose a robust functional Cox regression model that addresses this limitation. The proposed methodology combines a projection-pursuit-based robust functional principal component analysis with robust Cox regression estimation in a finite-dimensional subspace. By adopting the robust functional principal component analysis approach for dimension reduction, we obtain principal components that resist the influence of outlying functional observations. Then, a robust partial likelihood approach which additionally downweights the effects of outliers is used to estimate the parameters of a Cox regression model constructed using the robust functional principal components and scalar covariates. We establish the asymptotic properties of the proposed estimator, including Fisher consistency, [Formula: see text]-consistency, and asymptotic normality, under a set of mild and practically verifiable regularity conditions. Furthermore, we derive and analyze the influence function to assess the robustness characteristics of the estimator. Through an extensive Monte Carlo simulation study, we provide compelling evidence that the proposed method outperforms classical functional linear Cox regression and penalized functional regression techniques, particularly in the presence of outliers. We further demonstrate the proposed method's effectiveness using accelerometry-based survival data from the National Health and Nutrition Examination Survey. Our method has been implemented in the [Formula: see text] package.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147272812","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 treatment effects on duration with disease: a principal stratification framework. 估计治疗对病程的影响：一个主要的分层框架。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-02-17 DOI: 10.1007/s10985-025-09681-y

Erik T Parner

{"title":"Estimating treatment effects on duration with disease: a principal stratification framework.","authors":"Erik T Parner","doi":"10.1007/s10985-025-09681-y","DOIUrl":"10.1007/s10985-025-09681-y","url":null,"abstract":"In clinical research, estimating the average treatment effect is a common goal. However, when treatment effects vary substantially across individuals, it is often more informative to evaluate the treatment effect within subgroups. This paper focuses on causal inference for a duration outcome in a principal stratum-defined as the subgroup of individuals who would experience a positive duration under one treatment. Motivated by the Danish Vulva Cancer Recurrence Study (DaVulvaRec), which compares intensive versus standard follow-up in women treated for vulvar cancer, we examine the effect of intensive follow-up on the time with a cancer recurrence diagnosis. The principal stratum is in this example women who would be diagnosed with cancer recurrence under the intensive follow-up. We present a framework for identifying and estimating the average treatment effect in the principal stratum under a monotonicity assumption and introduce a sensitivity parameter to evaluate the impact of potential violations of this assumption. Using a multi-state model with pseudo-observations, we account for censoring and demonstrate that this approach offers greater statistical power than conventional comparisons between treatment groups. We illustrate the methodology to sample size calculation, the final analysis of the DaVulvaRec study using a simulated data set and an application to data from a randomized study on colon cancer.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 1","pages":"15"},"PeriodicalIF":1.5,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12913301/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146214810","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

Generalized win-odds regression models for composite endpoints. 复合终点的广义胜率回归模型。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-02-09 DOI: 10.1007/s10985-026-09693-2

Bang Wang, Zi Wang, Yu Cheng

{"title":"Generalized win-odds regression models for composite endpoints.","authors":"Bang Wang, Zi Wang, Yu Cheng","doi":"10.1007/s10985-026-09693-2","DOIUrl":"10.1007/s10985-026-09693-2","url":null,"abstract":"The time-to-first-event analysis is often used for studies involving multiple event times, where each component is treated equally, regardless of their clinical importance. Alternative summaries such as Win Ratio, Net Benefit, and Win Odds (WO) have drawn attention lately because they can handle different types of outcomes and allow for a hierarchical ordering in component outcomes. In this paper, we focus on WO and propose proportional WO regression models to evaluate the treatment effect on multiple outcomes while controlling for other risk factors. The models are easily interpretable as a standard logistic regression model. However, the proposed WO regression is more advanced; multiple outcomes of different types can be modeled together, and the estimating equation is constructed based on all possible and potentially dependent pairings of a treated individual with a control one under the functional response modeling framework. In addition, informative ties are carefully distinguished from those inconclusive comparisons due to censoring, and the latter is handled via the inverse probability of censoring weighting method. We establish the asymptotic properties of the estimated regression coefficients using the U-statistic theory and demonstrate the finite sample performance through numerical studies.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 1","pages":"13"},"PeriodicalIF":1.5,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146143989","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

Deep learning for the change-point Cox model with current status data. 基于当前状态数据的深度学习变点Cox模型。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-02-09 DOI: 10.1007/s10985-026-09689-y

Qiyue Huang, Anyin Feng, Qiang Wu, Xingwei Tong

引用次数: 0

Wasserstein GAN-based estimation for conditional distribution function with current status data. 基于Wasserstein gan的当前状态数据条件分布函数估计。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-01-31 DOI: 10.1007/s10985-026-09691-4

Wen Su, Changyu Liu, Guosheng Yin, Jian Huang

{"title":"Wasserstein GAN-based estimation for conditional distribution function with current status data.","authors":"Wen Su, Changyu Liu, Guosheng Yin, Jian Huang","doi":"10.1007/s10985-026-09691-4","DOIUrl":"10.1007/s10985-026-09691-4","url":null,"abstract":"Current status data are commonly encountered in modern medicine, econometrics and social science. Its unique characteristics pose significant challenges to the analysis of such data and the existing methods often suffer grave consequences when the underlying model is misspecified. To address these difficulties, we propose a model-free two-stage generative approach for estimating the conditional cumulative distribution function given predictors. We first learn a conditional generator nonparametrically for the joint conditional distribution of observation times and event status, and then construct the nonparametric maximum likelihood estimators of conditional distribution functions based on samples from the conditional generator. Subsequently, we study the convergence properties of the proposed estimator and establish its consistency. Simulation studies under various settings show the superior performance of the deep conditional generative approach over the classical modeling approaches and an application to Parvovirus B19 seroprevalence data yields reasonable predictions.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 1","pages":"12"},"PeriodicalIF":1.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12858558/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094778","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

Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes. 估计可解释的异质性治疗效果与生存结果的因果亚组发现。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-01-31 DOI: 10.1007/s10985-026-09688-z

Na Bo, Ying Ding

{"title":"Estimation of the interpretable heterogeneous treatment effect with causal subgroup discovery in survival outcomes.","authors":"Na Bo, Ying Ding","doi":"10.1007/s10985-026-09688-z","DOIUrl":"10.1007/s10985-026-09688-z","url":null,"abstract":"Estimating heterogeneous treatment effects (HTE) for survival outcomes has gained increasing attention in precision medicine, as it captures variations in treatment efficacy among patients or subgroups. However, most existing methods conduct post-hoc subgroup identifications rather than simultaneously estimating HTE and identifying causal subgroups. In this paper, we propose an interpretable HTE estimation framework that integrates meta-learners with tree-based methods to estimate the conditional average treatment effect (CATE) for survival outcomes and identify predictive subgroups simultaneously. We evaluated the performance of our method through extensive simulation studies. We also demonstrated its application in a large randomized controlled trial (RCT) for age-related macular degeneration (AMD), a progressive polygenic eye disease, to estimate the HTE of an antioxidant and mineral supplement on time-to-AMD progression and to identify genetically defined subgroups with enhanced treatment effects. Our method offers a direct interpretation of the estimated HTE and provides evidence to support precision healthcare.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 1","pages":"11"},"PeriodicalIF":1.5,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146094759","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

On Multiple Time Scales and Collapsibility. 关于多时间尺度和可折叠性。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-01-23 DOI: 10.1007/s10985-026-09687-0

David Oakes

引用次数: 0

Deep tobit model: an integrated framework for high-dimensional censored regression with variable selection. 深度tobit模型：一个具有变量选择的高维截尾回归的集成框架。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-01-23 DOI: 10.1007/s10985-026-09690-5

Tong Wu, Jiawen Hu, Zhi-Sheng Ye, Nan Chen

引用次数: 0

Beyond Bonferroni: new multiple contrast tests for time-to-event data under non-proportional hazards. 超越Bonferroni：在非比例风险下对事件时间数据的新的多重对比测试。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-01-14 DOI: 10.1007/s10985-025-09676-9

Ina Dormuth, Carolin Herrmann, Frank Konietschke, Markus Pauly, Matthias Wirth, Marc Ditzhaus

{"title":"Beyond Bonferroni: new multiple contrast tests for time-to-event data under non-proportional hazards.","authors":"Ina Dormuth, Carolin Herrmann, Frank Konietschke, Markus Pauly, Matthias Wirth, Marc Ditzhaus","doi":"10.1007/s10985-025-09676-9","DOIUrl":"10.1007/s10985-025-09676-9","url":null,"abstract":"When comparing multiple groups in clinical trials, we are not only interested in whether there is a difference between any groups but rather where the difference is. Such research questions lead to testing multiple individual hypotheses. To control the familywise error rate (FWER), we must apply some corrections or introduce tests that control the FWER by design. In the case of time-to-event data, a Bonferroni-corrected log-rank test is commonly used. This approach has two significant drawbacks: (i) it loses power when the proportional hazards assumption is violated and (ii) the correction generally leads to a lower power, especially when the test statistics are not independent. We propose two new tests based on combined weighted log-rank tests. One is a simple multiple contrast test of weighted log-rank tests, and one is an extension of the so-called CASANOVA test. The latter was introduced for factorial designs. We propose a new multiple contrast test based on the CASANOVA approach. Our test shows promise of being more powerful under crossing hazards and eliminates the need for additional p-value correction. We assess the performance of our tests through extensive Monte Carlo simulation studies covering both proportional and non-proportional hazard scenarios. Finally, we apply the new and reference methods to a real-world data example. The new approaches control the FWER and show reasonable power in all scenarios. They outperform the adjusted approaches in some non-proportional settings in terms of power.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 1","pages":"8"},"PeriodicalIF":1.5,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12804333/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145967358","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

Confidence intervals for high-dimensional accelerated failure time models under measurement errors. 测量误差下高维加速失效时间模型的置信区间。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-01-05 DOI: 10.1007/s10985-025-09685-8

Qin Yu, Xin Zhou, Jia Zhou, Zemin Zheng

引用次数: 0