Lifetime Data Analysis最新文献

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 : 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":"https://doi.org/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":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-10-23","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

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 : 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":"https://doi.org/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":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-10-13","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

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 : 2024-10-12 DOI: 10.1007/s10985-024-09637-8

Weiyu Fang, Jie Zhou, Mengqi Xie

引用次数: 0

Optimal survival analyses with prevalent and incident patients. 流行病患者和事故患者的最佳生存分析。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-12 DOI: 10.1007/s10985-024-09639-6

Nicholas Hartman

{"title":"Optimal survival analyses with prevalent and incident patients.","authors":"Nicholas Hartman","doi":"10.1007/s10985-024-09639-6","DOIUrl":"https://doi.org/10.1007/s10985-024-09639-6","url":null,"abstract":"Period-prevalent cohorts are often used for their cost-saving potential in epidemiological studies of survival outcomes. Under this design, prevalent patients allow for evaluations of long-term survival outcomes without the need for long follow-up, whereas incident patients allow for evaluations of short-term survival outcomes without the issue of left-truncation. In most period-prevalent survival analyses from the existing literature, patients have been recruited to achieve an overall sample size, with little attention given to the relative frequencies of prevalent and incident patients and their statistical implications. Furthermore, there are no existing methods available to rigorously quantify the impact of these relative frequencies on estimation and inference and incorporate this information into study design strategies. To address these gaps, we develop an approach to identify the optimal mix of prevalent and incident patients that maximizes precision over the entire estimated survival curve, subject to a flexible weighting scheme. In addition, we prove that inference based on the weighted log-rank test or Cox proportional hazards model is most powerful with an entirely prevalent or incident cohort, and we derive theoretical formulas to determine the optimal choice. Simulations confirm the validity of the proposed optimization criteria and show that substantial efficiency gains can be achieved by recruiting the optimal mix of prevalent and incident patients. The proposed methods are applied to assess waitlist outcomes among kidney transplant candidates.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142479189","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

Copula-based analysis of dependent current status data with semiparametric linear transformation model. 利用半参数线性变换模型对依赖性时态数据进行基于 Copula 的分析。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-08-24 DOI: 10.1007/s10985-024-09632-z

Huazhen Yu, Rui Zhang, Lixin Zhang

{"title":"Copula-based analysis of dependent current status data with semiparametric linear transformation model.","authors":"Huazhen Yu, Rui Zhang, Lixin Zhang","doi":"10.1007/s10985-024-09632-z","DOIUrl":"10.1007/s10985-024-09632-z","url":null,"abstract":"This paper discusses regression analysis of current status data with dependent censoring, a problem that often occurs in many areas such as cross-sectional studies, epidemiological investigations and tumorigenicity experiments. Copula model-based methods are commonly employed to tackle this issue. However, these methods often face challenges in terms of model and parameter identification. The primary aim of this paper is to propose a copula-based analysis for dependent current status data, where the association parameter is left unspecified. Our method is based on a general class of semiparametric linear transformation models and parametric copulas. We demonstrate that the proposed semiparametric model is identifiable under certain regularity conditions from the distribution of the observed data. For inference, we develop a sieve maximum likelihood estimation method, using Bernstein polynomials to approximate the nonparametric functions involved. The asymptotic consistency and normality of the proposed estimators are established. Finally, to demonstrate the effectiveness and practical applicability of our method, we conduct an extensive simulation study and apply the proposed method to a real data example.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047379","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

Nested case-control sampling without replacement. 无替换的嵌套病例对照抽样。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-09-05 DOI: 10.1007/s10985-024-09633-y

Yei Eun Shin, Takumi Saegusa

{"title":"Nested case-control sampling without replacement.","authors":"Yei Eun Shin, Takumi Saegusa","doi":"10.1007/s10985-024-09633-y","DOIUrl":"10.1007/s10985-024-09633-y","url":null,"abstract":"Nested case-control design (NCC) is a cost-effective outcome-dependent design in epidemiology that collects all cases and a fixed number of controls at the time of case diagnosis from a large cohort. Due to inefficiency relative to full cohort studies, previous research developed various estimation methodologies but changing designs in the formulation of risk sets was considered only in view of potential bias in the partial likelihood estimation. In this paper, we study a modified design that excludes previously selected controls from risk sets in view of efficiency improvement as well as bias. To this end, we extend the inverse probability weighting method of Samuelsen which was shown to outperform the partial likelihood estimator in the standard setting. We develop its asymptotic theory and a variance estimation of both regression coefficients and the cumulative baseline hazard function that takes account of the complex feature of the modified sampling design. In addition to good finite sample performance of variance estimation, simulation studies show that the modified design with the proposed estimator is more efficient than the standard design. Examples are provided using data from NIH-AARP Diet and Health Cohort Study.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134285","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

A flexible time-varying coefficient rate model for panel count data. 面板计数数据的灵活时变系数率模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-05-28 DOI: 10.1007/s10985-024-09630-1

Dayu Sun, Yuanyuan Guo, Yang Li, Jianguo Sun, Wanzhu Tu

引用次数: 0

Call for papers for a special issue on survival analysis in artificial intelligence. 人工智能生存分析特刊征稿启事。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-10-16 DOI: 10.1007/s10985-024-09636-9

Xingqiu Zhao

引用次数: 0

Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease. 对终末期肾病的纵向和生存结果进行时空多层次联合建模。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-10-04 DOI: 10.1007/s10985-024-09635-w

Esra Kürüm, Danh V Nguyen, Qi Qian, Sudipto Banerjee, Connie M Rhee, Damla Şentürk

{"title":"Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease.","authors":"Esra Kürüm, Danh V Nguyen, Qi Qian, Sudipto Banerjee, Connie M Rhee, Damla Şentürk","doi":"10.1007/s10985-024-09635-w","DOIUrl":"10.1007/s10985-024-09635-w","url":null,"abstract":"Individuals with end-stage kidney disease (ESKD) on dialysis experience high mortality and excessive burden of hospitalizations over time relative to comparable Medicare patient cohorts without kidney failure. A key interest in this population is to understand the time-dynamic effects of multilevel risk factors that contribute to the correlated outcomes of longitudinal hospitalization and mortality. For this we utilize multilevel data from the United States Renal Data System (USRDS), a national database that includes nearly all patients with ESKD, where repeated measurements/hospitalizations over time are nested in patients and patients are nested within (health service) regions across the contiguous U.S. We develop a novel spatiotemporal multilevel joint model (STM-JM) that accounts for the aforementioned hierarchical structure of the data while considering the spatiotemporal variations in both outcomes across regions. The proposed STM-JM includes time-varying effects of multilevel (patient- and region-level) risk factors on hospitalization trajectories and mortality and incorporates spatial correlations across the spatial regions via a multivariate conditional autoregressive correlation structure. Efficient estimation and inference are performed via a Bayesian framework, where multilevel varying coefficient functions are targeted via thin-plate splines. The finite sample performance of the proposed method is assessed through simulation studies. An application of the proposed method to the USRDS data highlights significant time-varying effects of patient- and region-level risk factors on hospitalization and mortality and identifies specific time periods on dialysis and spatial locations across the U.S. with elevated hospitalization and mortality risks.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376249","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

Unifying mortality forecasting model: an investigation of the COM–Poisson distribution in the GAS model for improved projections 统一死亡率预测模型：为改进预测而对 GAS 模型中 COM-Poisson 分布的研究

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-09-13 DOI: 10.1007/s10985-024-09634-x

Suryo Adi Rakhmawan, Tahir Mahmood, Nasir Abbas, Muhammad Riaz

{"title":"Unifying mortality forecasting model: an investigation of the COM–Poisson distribution in the GAS model for improved projections","authors":"Suryo Adi Rakhmawan, Tahir Mahmood, Nasir Abbas, Muhammad Riaz","doi":"10.1007/s10985-024-09634-x","DOIUrl":"https://doi.org/10.1007/s10985-024-09634-x","url":null,"abstract":"Forecasting mortality rates is crucial for evaluating life insurance company solvency, especially amid disruptions caused by phenomena like COVID-19. The Lee–Carter model is commonly employed in mortality modelling; however, extensions that can encompass count data with diverse distributions, such as the Generalized Autoregressive Score (GAS) model utilizing the COM–Poisson distribution, exhibit potential for enhancing time-to-event forecasting accuracy. Using mortality data from 29 countries, this research evaluates various distributions and determines that the COM–Poisson model surpasses the Poisson, binomial, and negative binomial distributions in forecasting mortality rates. The one-step forecasting capability of the GAS model offers distinct advantages, while the COM–Poisson distribution demonstrates enhanced flexibility and versatility by accommodating various distributions, including Poisson and negative binomial. Ultimately, the study determines that the COM–Poisson GAS model is an effective instrument for examining time series data on mortality rates, particularly when facing time-varying parameters and non-conventional data distributions.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220139","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