Lifetime Data Analysis最新文献_第2页

Transition dynamics modeling of pre-trained accelerometry representations for time to diagnosis of Parkinson's disease. 预先训练的加速度计表征到帕金森病诊断时间的过渡动力学建模。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-08-03 DOI: 10.1007/s10985-026-09721-1

Huiming Xie, Fei Xue, Xiao Wang

{"title":"Transition dynamics modeling of pre-trained accelerometry representations for time to diagnosis of Parkinson's disease.","authors":"Huiming Xie, Fei Xue, Xiao Wang","doi":"10.1007/s10985-026-09721-1","DOIUrl":"10.1007/s10985-026-09721-1","url":null,"abstract":"Early identification of Parkinson's disease is critical for timely intervention. Disruptions in sleep architecture and changes in physical activity patterns have been reported years before motor symptom onset, yet prodromal behavioral changes spanning sleep and daytime activity patterns are subtle and difficult to detect with conventional clinical tools. Wearable sensors provide a scalable means of monitoring these behaviors in natural settings, but extracting meaningful, interpretable features from high-frequency, unlabeled time series remains a major challenge. We present an end-to-end framework for assessing Parkinson's disease risk from wrist-worn accelerometry via automated feature extraction and survival modeling of time to diagnosis. Behavioral states are derived from unlabeled data using pretrained models including random forests with hidden Markov models for physical activity classification and a self-supervised learning based sleep staging model. We jointly model both sleep stage and physical activity sequences using hierarchical nonstationary Markov chains stratified by time of day and temporal resolution, characterizing individual-level behavioral rhythms and transition dynamics. Gradient-boosted Cox proportional hazards models are then used to estimate Parkinson's disease risk from these transition-based features. Applied to accelerometer data from the UK Biobank, our approach outperforms baselines that exclude dynamic modeling or rely on traditional functional data analysis, while providing interpretable predictors from long sequences of wearable sensor data. This demonstrates the potential of integrating pretrained AI models, temporal sequence modeling, and survival analysis to detect early behavioral signatures of neurodegeneration.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433425/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148664765","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

Backward joint model for the joint dynamic prediction of time-to-event and longitudinal data: basic formulation and new developments. 面向时间事件和纵向数据的关节动态预测的后向关节模型：基本公式及新进展。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-08-03 DOI: 10.1007/s10985-026-09725-x

Wenhao Li, Shikun Wang, Zhe Yin, Brad C Astor, Wei Yang, Tom H Greene, Liang Li

{"title":"Backward joint model for the joint dynamic prediction of time-to-event and longitudinal data: basic formulation and new developments.","authors":"Wenhao Li, Shikun Wang, Zhe Yin, Brad C Astor, Wei Yang, Tom H Greene, Liang Li","doi":"10.1007/s10985-026-09725-x","DOIUrl":"10.1007/s10985-026-09725-x","url":null,"abstract":"Dynamic prediction of future clinical outcomes based on longitudinally measured predictors plays a crucial role in disease management and patient counseling, particularly when conventional static models are inadequate. Joint modeling of longitudinal and time-to-event data provides a useful framework for addressing this challenge. In this paper, we present a comprehensive development of the recently proposed backward joint model (BJM; Shen and Li, 2021), which factorizes the likelihood into the distribution of time-to-event data and the conditional distribution of longitudinal data given the event time. This structure facilitates computation and is well-suited for multivariate longitudinal data. We introduce several novel developments to the BJM, including the extrapolation and two-part specifications, as well as the incorporation of competing risks. We also address an important yet underexplored problem in the literature: predicting future longitudinal trajectories conditional on predicted event times. Additionally, we explore the connection between BJM and existing joint modeling approaches. All these extensions preserve the computational advantages of the basic BJM formulation, including one-dimensional numerical integration, convex optimization via the EM algorithm, and a quick procedure for consistent estimation using standard software. We evaluate the method's performance through simulation studies and illustrate its utility in a chronic kidney disease application.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13433653/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148664744","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

Causal effect on the number of life years lost due to a specific event: average treatment effect and variable importance. 因特定事件导致的生命年数减少的因果效应：平均治疗效果和可变重要性。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-07-31 DOI: 10.1007/s10985-026-09712-2

Simon Christoffer Ziersen, Torben Martinussen

{"title":"Causal effect on the number of life years lost due to a specific event: average treatment effect and variable importance.","authors":"Simon Christoffer Ziersen, Torben Martinussen","doi":"10.1007/s10985-026-09712-2","DOIUrl":"10.1007/s10985-026-09712-2","url":null,"abstract":"Competing risk is a common phenomenon when dealing with time-to-event outcomes in biostatistical applications. An attractive estimand in this setting is the \"number of life-years lost due to a specific cause of death\". It provides a direct interpretation on the time-scale on which the data is observed. In this paper, we introduce the causal effect on the number of life years lost due to a specific event and give assumptions under which the average treatment effect (ATE) and the conditional average treatment effect (CATE) are identified from the observed data. Semiparametric estimators for the ATE and a partially linear projection of CATE, serving as a variable importance measure, are proposed. These estimators leverage machine learning for nuisance parameters and are model-agnostic, asymptotically normal, and efficient. We give conditions under which the estimators are asymptotically normal, and their performance is investigated in a simulation study. Lastly, the methods are implemented in a study concerning the response to different antidepressants using data from the Danish national registers.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13427853/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148632474","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

Correction to: Nonparametric estimation of a state entry time distribution conditional on a "past" state occupation in a progressive multistate model with current status data. 修正：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 : 2026-07-25 DOI: 10.1007/s10985-026-09719-9

Samuel Anyaso-Samuel, Somnath Datta

引用次数: 0

Threshold regression based on geometric Brownian motion with application to stock market prices. 基于几何布朗运动的阈值回归及其在股票市场价格中的应用。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-07-18 DOI: 10.1007/s10985-026-09720-2

Siwarak Sawongnam, Jiraphan Suntornchost

{"title":"Threshold regression based on geometric Brownian motion with application to stock market prices.","authors":"Siwarak Sawongnam, Jiraphan Suntornchost","doi":"10.1007/s10985-026-09720-2","DOIUrl":"10.1007/s10985-026-09720-2","url":null,"abstract":"The threshold regression model, first introduced by Lee and Whitmore in 2006 by combining the concepts of the first hitting time model and linear regression, has been widely applied in the medical context. The model features enhanced capabilities in incorporating information from auxiliary variables to model the target variable. These advances are evident not only in the medical context, but also in finance. Therefore, in this study, we extend the concept of the threshold regression model to financial data focusing on stock market prices. We construct a threshold regression model based on geometric Brownian motion, the conventional underlying process for the first hitting time models in financial data. We demonstrate advantages of the proposed model via applications to the stock exchange of Thailand (SET) compared with three reference models including the first hitting time model based on Brownian motion, the first hitting time model based on geometric Brownian motion, and the threshold regression model based on Brownian motion. Moreover, we enhance the quality of the proposed model by adapting a mixture cure fraction to the model. The results indicate that the proposed threshold regression based on geometric Brownian motion with a cure fraction outperforms other competing models.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148498326","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

Kolmogorov-Smirnov-type test for dependently double-truncated durations: A copula approach. 依赖双截断持续时间的kolmogorov - smirnov型检验：一种联结方法。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-07-18 DOI: 10.1007/s10985-026-09722-0

Anne-Marie Toparkus, Rafael Weißbach

{"title":"Kolmogorov-Smirnov-type test for dependently double-truncated durations: A copula approach.","authors":"Anne-Marie Toparkus, Rafael Weißbach","doi":"10.1007/s10985-026-09722-0","DOIUrl":"10.1007/s10985-026-09722-0","url":null,"abstract":"Given a double-truncated sample of lifespans, we test the hypothesis of a parametric distribution family for the lifespan. Demography typically certifies the life expectancy to be nonstationary in time. We model the resulting dependence between a lifespan and the birthday of an individual with a copula. Our main example is the Farlie-Gumbel-Morgenstern copula. The asymptotic null distribution of the test is based on Donsker-class arguments and the functional delta method for empirical processes. One assumption for the test is consistency in the estimation of the parameter for the model under the null hypothesis. Requirements for consistency are fulfilled by our set of assumptions. The test is carried out in two stages, the computation of the test statistics and the simulation of the critical value. The statistic can be reached after finitely many computation steps, whereas the critical value can only be an approximation. With the exponential distribution as an example for the lifespan distribution, and for the application to 55,000 German double-truncated enterprise lifespans, the constructed Kolmogorov-Smirnov test rejects clearly an age-homogeneous closure hazard.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13380589/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148498329","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

Shared frailty sieve estimation for dependent left truncated and interval censored data. 相关左截断和区间截尾数据的共享脆弱筛估计。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-06-27 DOI: 10.1007/s10985-026-09718-w

Muhammad Mustapha, Zarina Mohd Khalid

引用次数: 0

Functional win-fractions regression models for composite outcomes. 复合结果的功能win-fractions回归模型。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-06-10 DOI: 10.1007/s10985-026-09716-y

Abhisek Chakraborty, Abhishek Mandal

{"title":"Functional win-fractions regression models for composite outcomes.","authors":"Abhisek Chakraborty, Abhishek Mandal","doi":"10.1007/s10985-026-09716-y","DOIUrl":"10.1007/s10985-026-09716-y","url":null,"abstract":"In clinical trials with prioritized composite outcomes, win ratios are commonly employed to evaluate the efficacy of investigational interventions. Adjusting such win ratios with respect to covariates enhances the accuracy and precision of treatment effect estimates, by controlling for baseline clinical characteristics, demographics, etc. Effects of the covariates on composite outcomes are often of complex and non-linear nature, reflecting the complexity of underlying biological mechanisms. Parametric approaches that assume an additive effect of covariates on the log-hazard often fail to capture such complexities, resulting in unreliable inferences and reduced predictive accuracy. In this article, we introduce a flexible win fraction regression framework based on [Formula: see text]-splines, that is capable of assessing the extent and nature of the functional effects of covariates on the outcome adaptively. By leveraging the moment condition model framework equipped with the generalized method of moments (GMM) technique, we develop an efficient computational algorithm to carry out inference based on the proposed model. Using the asymptotic distribution of the estimated spline coefficients, we develop a large-sample test to assess the significance of the functional covariates. The favorable operating characteristics of the proposed methodology, compared to the current state-of-the-art, are assessed through extensive simulations. Finally, the practical utility of our proposal is demonstrated through the analysis of composite time-to-event datasets arising from cardiovascular and breast cancer clinical trials.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"32 3","pages":""},"PeriodicalIF":1.5,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148213119","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

Variable selection in causal semiparametric transformation models with all-or-nothing treatment compliance. 具有全或无处理依从性的因果半参数转换模型的变量选择。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-06-05 DOI: 10.1007/s10985-026-09696-z

Lijun Fang, Shuwei Li, Tao Hu

引用次数: 0

Correction to: A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model. 修正：用于相位型老化模型的推理相关估计的均匀化驱动算法。

IF 1.5 3区数学

Lifetime Data Analysis Pub Date : 2026-06-03 DOI: 10.1007/s10985-025-09684-9

Boquan Cheng, Bruce Jones, Xiaoming Liu, Rogemar Mamon

引用次数: 0