Lifetime Data Analysis最新文献_第4页

Comparing adaptive treatment strategies in two-stage randomized trials with grouped survival data. 比较两期随机试验与分组生存数据的适应性治疗策略。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-04-02 DOI: 10.1007/s10985-026-09708-y

Monique Sparkman, Susan Halabi, Zhiguo Li

{"title":"Comparing adaptive treatment strategies in two-stage randomized trials with grouped survival data.","authors":"Monique Sparkman, Susan Halabi, Zhiguo Li","doi":"10.1007/s10985-026-09708-y","DOIUrl":"10.1007/s10985-026-09708-y","url":null,"abstract":"Two-stage randomized trials, or the more general sequential multiple assignment randomized trials (SMART), have been increasingly used in studying adaptive treatment strategies for treating chronic diseases or conditions where treatments need to be adjusted during the treatment course. Methods for analyzing two-stage randomized trials with continuous, longitudinal or right-censored survival endpoints have been developed. However, no method exists for data analysis for two-stage randomized trials with grouped survival endpoints, which are frequently encountered in practice. In this article, we propose methods for analyzing grouped survival data from two-stage randomized trials. We first extend the methods in Prentice and Gloeckler (1978) to allow for patient missing pre-specified visits, and use an efficient score test to make inferences on treatment effects with grouped data in traditional randomized trials. Based on this, we propose a weighted efficient score test to compare two adaptive treatment strategies with grouped survival data in two-stage randomized trials with missing visits. Asymptotic properties are formulated, and simulation studies are conducted to evaluate the performances of the proposed methods. We also apply the weighted score test in analyzing data from the sequenced treatment alternatives to relieve depression (STAR*D) trial.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13123648/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147595989","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

Nonparametric estimation of average effects of a continuous treatment for survival data with a cured fraction. 对带有治愈分数的生存数据的连续治疗的平均效果的非参数估计。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-03-31 DOI: 10.1007/s10985-026-09703-3

Hang Liu, Yingwei Peng

{"title":"Nonparametric estimation of average effects of a continuous treatment for survival data with a cured fraction.","authors":"Hang Liu, Yingwei Peng","doi":"10.1007/s10985-026-09703-3","DOIUrl":"10.1007/s10985-026-09703-3","url":null,"abstract":"Estimating the causal effect of a continuous treatment on survival data, particularly in cases where there is a cured fraction from observational studies, is a significant issue. However, this topic is not well addressed in the existing literature. Current methods either rely on strong parametric assumptions or struggle to effectively control for confounding variables. In this study, we propose a novel nonparametric estimation method that utilizes a weighted generalized Kaplan-Meier survival estimator. This method aims to estimate the average effects of a continuous treatment on both the probability of being cured and the restricted mean survival time. Notably, our approach does not require any parametric assumptions about the effects, and it can efficiently control for multiple confounding variables. A simulation study demonstrates that our proposed method outperforms existing approaches, particularly when the average effects are complex or when confounding is strong. We apply this method to data from a study of chlamydia patients to evaluate the average effects of years of schooling on the probability of being immune to reinfection, as well as on the restricted mean survival time.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147582993","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 attributable risk functions for censored time-to-event in disease prevention research. 估计疾病预防研究中审查时间到事件的归因风险函数。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-03-27 DOI: 10.1007/s10985-026-09704-2

Ying Qing Chen, Yixin Wang, Xinyi Zhang, Ross L Prentice

引用次数: 0

Nonparametric estimation of conditional survival function with time-varying covariates using DeepONet. 时变协变量条件生存函数的非参数估计。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-03-24 DOI: 10.1007/s10985-026-09700-6

Bingqing Hu, Bin Nan

{"title":"Nonparametric estimation of conditional survival function with time-varying covariates using DeepONet.","authors":"Bingqing Hu, Bin Nan","doi":"10.1007/s10985-026-09700-6","DOIUrl":"10.1007/s10985-026-09700-6","url":null,"abstract":"Traditional survival models often rely on restrictive assumptions such as proportional hazards or instantaneous effects of time-varying covariates on the hazard function, which limit their applicability in real-world settings. We consider the nonparametric estimation of the conditional survival function, which leverages the flexibility of neural networks to capture the complex, potentially long-term non-instantaneous effects of time-varying covariates. In this work, we use Deep Operator Networks (DeepONet), a deep learning architecture designed for operator learning, to model the arbitrary effects of both time-varying and time-invariant covariates. Specifically, our method relaxes commonly used assumptions in hazard regressions by modeling the conditional hazard function as an unknown nonlinear operator of entire histories of time-varying covariates. The estimation is based on a loss function constructed from the nonparametric full likelihood for censored survival data. Simulation studies demonstrate that our method performs well, whereas the Cox model yields biased results when the assumption of instantaneous time-varying covariate effects is violated. We further illustrate its utility with the ADNI data, for which it yields a lower integrated Brier score than the Cox model.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13013183/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516014","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

Nonparametric estimation of a state entry time distribution conditional on a "past" state occupation in a progressive multistate model with current status data. 在具有当前状态数据的渐进多状态模型中，以“过去”状态职业为条件的状态进入时间分布的非参数估计

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-03-24 DOI: 10.1007/s10985-026-09699-w

Samuel Anyaso-Samuel, Somnath Datta

{"title":"Nonparametric estimation of a state entry time distribution conditional on a \"past\" state occupation in a progressive multistate model with current status data.","authors":"Samuel Anyaso-Samuel, Somnath Datta","doi":"10.1007/s10985-026-09699-w","DOIUrl":"10.1007/s10985-026-09699-w","url":null,"abstract":"Case-I interval-censored (current status) data from multistate systems are often encountered in biomedical and epidemiological studies. In this article, we focus on the problem of estimating state entry distribution and occupation probabilities, contingent on a preceding state occupation. This endeavor is particularly complex owing to the inherent challenge of the unavailability of directly observed counts of individuals at risk of transitioning from a state, due to severe interval censoring. We propose two nonparametric approaches, one using the fractional at-risk set approach recently adopted in the right-censoring framework and the other a new estimator based on the ratio of marginal state occupation probabilities. Both estimation approaches utilize innovative applications of concepts from the competing risks paradigm. The finite-sample behavior of the proposed estimators is studied via extensive simulation studies where we show that the estimators based on severely censored current status data have good performance when compared with those based on complete data. We demonstrate the application of the two methods to analyze data from patients diagnosed with breast cancer.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147515903","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

Partially linear Cox model with neural networks for left-truncated data. 左截断数据的神经网络部分线性Cox模型。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-03-24 DOI: 10.1007/s10985-026-09702-4

Shiying Li, Li Shao, Shuwei Li

{"title":"Partially linear Cox model with neural networks for left-truncated data.","authors":"Shiying Li, Li Shao, Shuwei Li","doi":"10.1007/s10985-026-09702-4","DOIUrl":"10.1007/s10985-026-09702-4","url":null,"abstract":"In the past few decades, artificial neural networks (ANNs) have exhibited their superior capabilities for capturing nonlinear data patterns in supervised learning. Inspired by such desirable advantages, we herein explore the integration of ANNs with partially linear Cox model in the context of left truncation. This sampling scheme tends to enroll individuals with slower disease progression and thus leads to a biased sample of survival times in the target population. The proposed model comprises both parametric covariate effects and a nuisance function of uninterested covariates that is approximated with ANNs, offering a balance between interpretability and flexibility. We consider the conditional maximum likelihood estimation and derive a profile likelihood that is free of the baseline hazard function. An iterative algorithm embedded with stochastic gradient descent is proposed to minimize the negative profile log-likelihood, leading to the estimators of the regression parameters and ANNs simultaneously. Extensive simulation studies and an application demonstrate that the proposed method outperforms the traditional approaches regarding the estimation accuracy and predictive capability.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147516132","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

Inference for cause-specific cox model absolute risk in cohort subsampling designs. 队列亚抽样设计中原因特异性cox模型绝对风险的推断。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-03-18 DOI: 10.1007/s10985-025-09675-w

Lola Etiévant, Mitchell H Gail

{"title":"Inference for cause-specific cox model absolute risk in cohort subsampling designs.","authors":"Lola Etiévant, Mitchell H Gail","doi":"10.1007/s10985-025-09675-w","DOIUrl":"10.1007/s10985-025-09675-w","url":null,"abstract":"The original case-cohort design obtains detailed covariate information on a random sample of subjects from the cohort (subcohort) and on the subjects who developed the event of interest (cases). Recently, there was some work on case-cohort estimation of pure risk, i.e., the hypothetical probability that the event occurs, assuming it is the only risk. But competing events can preclude the occurrence of the event of interest, and the pure risk thus overestimates the probability of experiencing the event of interest (absolute risk). Under the cause-specific hazard Cox model, methods for case-cohort inference have been published for relative hazards and cumulative baseline hazards; we have not seen treatments of absolute risk, however. In this work we focus on absolute risk inference under the cause-specific hazard Cox model when using a sample of subjects from the cohort. We propose an influence-based variance estimation formula and consider two sampling designs: (1) a case-cohort with exhaustive sampling of subjects who developed the event of interest or a competing event; and (2) an event-stratified sample of the cohort that only includes fractions of these subjects. Our proposed variance estimate properly accounts for the sampling features and allows appropriate analysis of the sampled data. We illustrate our method and designs in simulation and on the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial. These analyses also suggest that the \"robust\" variance originally proposed by Barlow (Biometrics, 50:1064-1072, 1994) may be too large for the absolute risk when using a cohort subsampling design.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147482127","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

Semiparametric regression analysis of interval-censored competing risks data under additive hazards model with missing event types. 缺失事件类型加性风险模型下区间剔除竞争风险数据的半参数回归分析。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-03-09 DOI: 10.1007/s10985-026-09698-x

Ruobing Jia, Yichen Lou, Jianguo Sun, Peijie Wang

引用次数: 0

Doubly robust g-estimation of structural nested cumulative survival time models with non-ignorable, non-monotone missing data in time-varying confounders. 具有时变混杂因素中不可忽略、非单调缺失数据的结构嵌套累积生存时间模型的双鲁棒g估计。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-03-04 DOI: 10.1007/s10985-026-09692-3

Yoshinori Takeuchi, Sho Komukai, Atsushi Goto, Tomohiro Shinozaki

{"title":"Doubly robust g-estimation of structural nested cumulative survival time models with non-ignorable, non-monotone missing data in time-varying confounders.","authors":"Yoshinori Takeuchi, Sho Komukai, Atsushi Goto, Tomohiro Shinozaki","doi":"10.1007/s10985-026-09692-3","DOIUrl":"10.1007/s10985-026-09692-3","url":null,"abstract":"To examine the causal effects of time-varying treatments on survival, structural nested cumulative survival time models (SNCSTMs) are flexible and theoretically promising semiparametric models characterized by causally interpretable parameters. One concern is the prerequisite for uniformly scheduled data collection and complete data for time-varying confounders. For example, in pharmacoepidemiological studies using medical information databases, laboratory test results can be missing due to unscheduled hospital visits or non-compliance with health checkups. Furthermore, missing mechanisms data may be non-ignorable and non-monotone, invalidating the typical missing-data methods that assume ignorable or monotone missing mechanisms. We propose a novel g-estimation method for SNCSTMs with non-ignorable, non-monotonic missing data for time-varying confounders. We augment the g-estimation functions using missing probability and imputation models, incorporating a user-defined selection function, which allows sensitivity analyses to evaluate the departure of missing data from ignorable mechanisms. Using a proper selection function, our estimator is doubly robust in the sense that it is consistent if either model for missing probability or imputation of missing data is correct at each time point and if either model for propensity score or conditional expectation of counterfactual counting processes is correct. Moreover, applying frequentist-type multiple imputation yields a closed-form solution for calculating the estimator, even if time-varying confounders are missing. A simulation study evaluated our proposed method's finite sample performance and the estimator's double robustness. We also conducted sensitivity analyses in a pharmacoepidemiological study using a Japanese medical claims database, assessing the risk of hypoglycemia in sulfonylurea-treated patients with incomplete hemoglobin A1c values.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147357324","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

Optimal designs for discrete-time survival models with competing risks. 具有竞争风险的离散时间生存模型的最优设计。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-02-28 DOI: 10.1007/s10985-026-09695-0

XiaoDong Zhou, YunJuan Wang, RongXian Yue, Weng Kee Wong

{"title":"Optimal designs for discrete-time survival models with competing risks.","authors":"XiaoDong Zhou, YunJuan Wang, RongXian Yue, Weng Kee Wong","doi":"10.1007/s10985-026-09695-0","DOIUrl":"10.1007/s10985-026-09695-0","url":null,"abstract":"Current methodological research on randomized controlled trial design has predominantly focused on studies with a single primary endpoint. However, many trials in practice involve multiple competing target events. The optimal designs for survival trials with competing target events have not been systematically addressed in the statistical literature. This paper fills this significant gap by developing design methodologies for randomized discrete-time-to-event trials with competing endpoints. We derive the Fisher information matrix for the general discrete-time survival model (DTSM) by transforming the original discrete-time survival data into proper multinomial responses. By introducing a cost-based generalized [Formula: see text]-optimal design criterion, we identify various types of optimal designs for estimating the treatment effects. Under the assumption of a parametric competing risks model for the underlying survival process, we demonstrate that the optimal treatment allocation scheme is critically influenced by the parameter values within this model. Our methodology is applied to the redesign of the SANAD trial, which examines withdrawal times from anti-epileptic drugs, thereby highlighting the advantages of our optimal design strategies. A key finding is that assigning subjects equally to the different groups in a two-arm DTSM trial with competing risks is generally a favorable choice, unless the hazard rates over the duration of the trial in both groups are low.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12950086/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147318894","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