Lifetime Data Analysis最新文献_第2页

A global kernel estimator for partially linear varying coefficient additive hazards models. 部分线性变系数加性危害模型的全局核估计。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-01-01 Epub Date: 2025-01-09 DOI: 10.1007/s10985-024-09645-8

Hoi Min Ng, Kin Yau Wong

{"title":"A global kernel estimator for partially linear varying coefficient additive hazards models.","authors":"Hoi Min Ng, Kin Yau Wong","doi":"10.1007/s10985-024-09645-8","DOIUrl":"10.1007/s10985-024-09645-8","url":null,"abstract":"We study kernel-based estimation methods for partially linear varying coefficient additive hazards models, where the effects of one type of covariates can be modified by another. Existing kernel estimation methods for varying coefficient models often use a \"local\" approach, where only a small local neighborhood of subjects are used for estimating the varying coefficient functions. Such a local approach, however, is generally inefficient as information about some non-varying nuisance parameter from subjects outside the neighborhood is discarded. In this paper, we develop a \"global\" kernel estimator that simultaneously estimates the varying coefficients over the entire domains of the functions, leveraging the non-varying nature of the nuisance parameter. We establish the consistency and asymptotic normality of the proposed estimators. The theoretical developments are substantially more challenging than those of the local methods, as the dimension of the global estimator increases with the sample size. We conduct extensive simulation studies to demonstrate the feasibility and superior performance of the proposed methods compared with existing local methods and provide an application to a motivating cancer genomic study.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"205-232"},"PeriodicalIF":1.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142957993","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 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 : 2025-01-01 Epub 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":"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":" ","pages":"24-51"},"PeriodicalIF":1.2,"publicationDate":"2025-01-01","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

A class of semiparametric models for bivariate survival data. 二元生存数据的一类半参数模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-01-01 Epub Date: 2024-12-14 DOI: 10.1007/s10985-024-09642-x

Walmir Dos Reis Miranda Filho, Fábio Nogueira Demarqui

{"title":"A class of semiparametric models for bivariate survival data.","authors":"Walmir Dos Reis Miranda Filho, Fábio Nogueira Demarqui","doi":"10.1007/s10985-024-09642-x","DOIUrl":"10.1007/s10985-024-09642-x","url":null,"abstract":"We propose a new class of bivariate survival models based on the family of Archimedean copulas with margins modeled by the Yang and Prentice (YP) model. The Ali-Mikhail-Haq (AMH), Clayton, Frank, Gumbel-Hougaard (GH), and Joe copulas are employed to accommodate the dependency among marginal distributions. Baseline distributions are modeled semiparametrically by the Piecewise Exponential (PE) distribution and the Bernstein polynomials (BP). Inference procedures for the proposed class of models are based on the maximum likelihood (ML) approach. The new class of models possesses some attractive features: i) the ability to take into account survival data with crossing survival curves; ii) the inclusion of the well-known proportional hazards (PH) and proportional odds (PO) models as particular cases; iii) greater flexibility provided by the semiparametric modeling of the marginal baseline distributions; iv) the availability of closed-form expressions for the likelihood functions, leading to more straightforward inferential procedures. The properties of the proposed class are numerically investigated through an extensive simulation study. Finally, we demonstrate the versatility of our new class of models through the analysis of survival data involving patients diagnosed with ovarian cancer.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"102-125"},"PeriodicalIF":1.2,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142824540","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":" ","pages":"742-775"},"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":" ","pages":"776-799"},"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":" ","pages":"827-852"},"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":"60 1","pages":""},"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

Special issue dedicated to Mitchell H. Gail, M.D. Ph.D. 米切尔-盖尔医学博士特刊

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-06-24 DOI: 10.1007/s10985-024-09631-0

Mei-Ling Ting Lee

引用次数: 0