Lifetime Data Analysis最新文献_第2页

A pairwise pseudo-likelihood approach for regression analysis of doubly truncated data. 双截断数据回归分析的两两伪似然方法。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-03-31 DOI: 10.1007/s10985-025-09649-y

Cunjin Zhao, Peijie Wang, Jianguo Sun

引用次数: 0

Goodness-of-fit testing in the presence of cured data: IPCW approach. 存在固化数据的拟合优度检验：IPCW方法。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-03-04 DOI: 10.1007/s10985-025-09646-1

Marija Cuparić, Bojana Milošević

引用次数: 0

A flexible Bayesian g-formula for causal survival analyses with time-dependent confounding. 一个灵活的贝叶斯g公式的因果生存分析与时间相关的混淆。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-04-14 DOI: 10.1007/s10985-025-09652-3

Xinyuan Chen, Liangyuan Hu, Fan Li

引用次数: 0

Robust inverse probability weighted estimators for doubly truncated Cox regression with closed-form standard errors. 具有封闭标准误差的双截断Cox回归的鲁棒逆概率加权估计。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-04-15 DOI: 10.1007/s10985-025-09650-5

Omar Vazquez, Sharon X Xie

{"title":"Robust inverse probability weighted estimators for doubly truncated Cox regression with closed-form standard errors.","authors":"Omar Vazquez, Sharon X Xie","doi":"10.1007/s10985-025-09650-5","DOIUrl":"https://doi.org/10.1007/s10985-025-09650-5","url":null,"abstract":"Survival data is doubly truncated when only participants who experience an event during a random interval are included in the sample. Existing methods typically correct for double truncation bias in Cox regression through inverse probability weighting via the nonparametric maximum likelihood estimate (NPMLE) of the selection probabilities. This approach relies on two key assumptions, quasi-independent truncation and positivity of the sampling probabilities, yet there are no methods available to thoroughly assess these assumptions in the regression context. Furthermore, these estimators can be particularly sensitive to extreme event times. Finally, current double truncation methods rely on bootstrapping for variance estimation. Aside from the unnecessary computational burden, there are often identifiability issues with the NPMLE during bootstrap resampling. To address these limitations of current methods, we propose a class of robust Cox regression coefficient estimators with time-varying inverse probability weights and extend these estimators to conduct sensitivity analysis regarding possible non-positivity of the sampling probabilities. Also, we develop a nonparametric test and graphical diagnostic for verifying the quasi-independent truncation assumption. Finally, we provide closed-form standard errors for the NPMLE as well as for the proposed estimators. The proposed estimators are evaluated through extensive simulations and illustrated using an AIDS study.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"31 2","pages":"364-393"},"PeriodicalIF":1.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12043752/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144049810","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

Author correction to: "causal survival analysis under competing risks using longitudinal modified treatment policies". 作者更正：“使用纵向修正治疗政策的竞争风险下的因果生存分析”。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-04-01 Epub Date: 2025-04-14 DOI: 10.1007/s10985-025-09651-4

Iván Díaz, Nicholas Williams, Katherine L Hoffman, Nima S Hejazi

{"title":"Author correction to: \"causal survival analysis under competing risks using longitudinal modified treatment policies\".","authors":"Iván Díaz, Nicholas Williams, Katherine L Hoffman, Nima S Hejazi","doi":"10.1007/s10985-025-09651-4","DOIUrl":"https://doi.org/10.1007/s10985-025-09651-4","url":null,"abstract":"The published version of the manuscript (D´iaz, Hoffman, Hejazi Lifetime Data Anal 30, 213-236, 2024) contained an error (We would like to thank Kara Rudolph for pointing out an issue that led to uncovering the error)) in the definition of the outcome that had cascading effects and created errors in the definition of multiple objects in the paper. We correct those errors here. For completeness, we reproduce the entire manuscript, underlining places where we made a correction.Longitudinal modified treatment policies (LMTP) have been recently developed as a novel method to define and estimate causal parameters that depend on the natural value of treatment. LMTPs represent an important advancement in causal inference for longitudinal studies as they allow the non-parametric definition and estimation of the joint effect of multiple categorical, ordinal, or continuous treatments measured at several time points. We extend the LMTP methodology to problems in which the outcome is a time-to-event variable subject to a competing event that precludes observation of the event of interest. We present identification results and non-parametric locally efficient estimators that use flexible data-adaptive regression techniques to alleviate model misspecification bias, while retaining important asymptotic properties such as <math><msqrt><mi>n</mi></msqrt> </math> -consistency. We present an application to the estimation of the effect of the time-to-intubation on acute kidney injury amongst COVID- 19 hospitalized patients, where death by other causes is taken to be the competing event.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"31 2","pages":"442-471"},"PeriodicalIF":1.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144025067","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

Two-stage pseudo maximum likelihood estimation of semiparametric copula-based regression models for semi-competing risks data. 针对半竞争风险数据的半参数 copula 回归模型的两阶段伪极大似然估计。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-01-01 Epub Date: 2024-10-23 DOI: 10.1007/s10985-024-09640-z

Sakie J Arachchige, Xinyuan Chen, Qian M Zhou

{"title":"Two-stage pseudo maximum likelihood estimation of semiparametric copula-based regression models for semi-competing risks data.","authors":"Sakie J Arachchige, Xinyuan Chen, Qian M Zhou","doi":"10.1007/s10985-024-09640-z","DOIUrl":"10.1007/s10985-024-09640-z","url":null,"abstract":"We propose a two-stage estimation procedure for a copula-based model with semi-competing risks data, where the non-terminal event is subject to dependent censoring by the terminal event, and both events are subject to independent censoring. With a copula-based model, the marginal survival functions of individual event times are specified by semiparametric transformation models, and the dependence between the bivariate event times is specified by a parametric copula function. For the estimation procedure, in the first stage, the parameters associated with the marginal of the terminal event are estimated using only the corresponding observed outcomes, and in the second stage, the marginal parameters for the non-terminal event time and the copula parameter are estimated together via maximizing a pseudo-likelihood function based on the joint distribution of the bivariate event times. We derived the asymptotic properties of the proposed estimator and provided an analytic variance estimator for inference. Through simulation studies, we showed that our approach leads to consistent estimates with less computational cost and more robustness than the one-stage procedure developed in Chen YH (Lifetime Data Anal 18:36-57, 2012), where all parameters were estimated simultaneously. In addition, our approach demonstrates more desirable finite-sample performances over another existing two-stage estimation method proposed in Zhu H et al., (Commu Statistics-Theory Methods 51(22):7830-7845, 2021) . An R package PMLE4SCR is developed to implement our proposed method.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"52-75"},"PeriodicalIF":1.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142511911","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

Conditional modeling of recurrent event data with terminal event. 带有终端事件的循环事件数据条件建模。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-01-01 Epub Date: 2024-10-12 DOI: 10.1007/s10985-024-09637-8

Weiyu Fang, Jie Zhou, Mengqi Xie

引用次数: 0

Proportional rates model for recurrent event data with intermittent gaps and a terminal event. 具有间歇性间隙和终端事件的重复事件数据的比例率模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-01-01 Epub Date: 2024-12-16 DOI: 10.1007/s10985-024-09644-9

Jin Jin, Xinyuan Song, Liuquan Sun, Pei-Fang Su

引用次数: 0

Right-censored models by the expectile method. 期望法右删减模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-01-01 Epub Date: 2025-01-03 DOI: 10.1007/s10985-024-09643-w

Gabriela Ciuperca

引用次数: 0

Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference. 评估时间到事件真实终点的时间到事件替代物：基于因果推理的信息论方法。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-01-01 Epub Date: 2024-10-13 DOI: 10.1007/s10985-024-09638-7

Florian Stijven, Geert Molenberghs, Ingrid Van Keilegom, Wim Van der Elst, Ariel Alonso

{"title":"Evaluating time-to-event surrogates for time-to-event true endpoints: an information-theoretic approach based on causal inference.","authors":"Florian Stijven, Geert Molenberghs, Ingrid Van Keilegom, Wim Van der Elst, Ariel Alonso","doi":"10.1007/s10985-024-09638-7","DOIUrl":"10.1007/s10985-024-09638-7","url":null,"abstract":"Putative surrogate endpoints must undergo a rigorous statistical evaluation before they can be used in clinical trials. Numerous frameworks have been introduced for this purpose. In this study, we extend the scope of the information-theoretic causal-inference approach to encompass scenarios where both outcomes are time-to-event endpoints, using the flexibility provided by D-vine copulas. We evaluate the quality of the putative surrogate using the individual causal association (ICA)-a measure based on the mutual information between the individual causal treatment effects. However, in spite of its appealing mathematical properties, the ICA may be ill defined for composite endpoints. Therefore, we also propose an alternative rank-based metric for assessing the ICA. Due to the fundamental problem of causal inference, the joint distribution of all potential outcomes is only partially identifiable and, consequently, the ICA cannot be estimated without strong unverifiable assumptions. This is addressed by a formal sensitivity analysis that is summarized by the so-called intervals of ignorance and uncertainty. The frequentist properties of these intervals are discussed in detail. Finally, the proposed methods are illustrated with an analysis of pooled data from two advanced colorectal cancer trials. The newly developed techniques have been implemented in the R package Surrogate.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"1-23"},"PeriodicalIF":1.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479188","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