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 : 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

Nonparametric estimation of the cumulative incidence function for doubly-truncated and interval-censored competing risks data. 双截断和区间截断竞争风险数据的累积发病率函数的非参数估计。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2025-01-01 Epub Date: 2024-11-17 DOI: 10.1007/s10985-024-09641-y

Pao-Sheng Shen

引用次数: 0

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