Lifetime Data Analysis最新文献_第10页

A new approach to estimation of the proportional hazards model based on interval-censored data with missing covariates 一种基于缺失协变量区间截尾数据的比例风险模型估计新方法

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-03-29 DOI: 10.1007/s10985-022-09550-y

Ruiwen Zhou, Huiqiong Li, Jianguo Sun, Niansheng Tang

引用次数: 1

Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models 高维二元截尾回归模型的Bayesian惩罚Buckley-James方法

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-03-03 DOI: 10.1007/s10985-022-09549-5

Wenjing Yin, S. Zhao, Feng Liang

引用次数: 0

Prognostic accuracy for predicting ordinal competing risk outcomes using ROC surfaces. 使用ROC曲面预测有序竞争风险结果的预后准确性。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-01-01 Epub Date: 2021-11-22 DOI: 10.1007/s10985-021-09539-z

Song Zhang, Yang Qu, Yu Cheng, Oscar L Lopez, Abdus S Wahed

引用次数: 1

An additive hazards frailty model with semi-varying coefficients. 半变系数加性危险脆弱性模型。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-01-01 Epub Date: 2021-11-25 DOI: 10.1007/s10985-021-09540-6

Zhongwen Zhang, Xiaoguang Wang, Yingwei Peng

引用次数: 0

Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction. 具有非敏感分数的长度偏差和区间截尾数据的最大似然估计。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-01-01 Epub Date: 2021-10-08 DOI: 10.1007/s10985-021-09536-2

Pao-Sheng Shen, Yingwei Peng, Hsin-Jen Chen, Chyong-Mei Chen

{"title":"Maximum likelihood estimation for length-biased and interval-censored data with a nonsusceptible fraction.","authors":"Pao-Sheng Shen, Yingwei Peng, Hsin-Jen Chen, Chyong-Mei Chen","doi":"10.1007/s10985-021-09536-2","DOIUrl":"https://doi.org/10.1007/s10985-021-09536-2","url":null,"abstract":"Left-truncated data are often encountered in epidemiological cohort studies, where individuals are recruited according to a certain cross-sectional sampling criterion. Length-biased data, a special case of left-truncated data, assume that the incidence of the initial event follows a homogeneous Poisson process. In this article, we consider an analysis of length-biased and interval-censored data with a nonsusceptible fraction. We first point out the importance of a well-defined target population, which depends on the prior knowledge for the support of the failure times of susceptible individuals. Given the target population, we proceed with a length-biased sampling and draw valid inferences from a length-biased sample. When there is no covariate, we show that it suffices to consider a discrete version of the survival function for the susceptible individuals with jump points at the left endpoints of the censoring intervals when maximizing the full likelihood function, and propose an EM algorithm to obtain the nonparametric maximum likelihood estimates of nonsusceptible rate and the survival function of the susceptible individuals. We also develop a novel graphical method for assessing the stationarity assumption. When covariates are present, we consider the Cox proportional hazards model for the survival time of the susceptible individuals and the logistic regression model for the probability of being susceptible. We construct the full likelihood function and obtain the nonparametric maximum likelihood estimates of the regression parameters by employing the EM algorithm. The large sample properties of the estimates are established. The performance of the method is assessed by simulations. The proposed model and method are applied to data from an early-onset diabetes mellitus study.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 1","pages":"68-88"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39497382","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: 5

Nonparametric inference in the accelerated failure time model using restricted means. 基于受限方法的加速失效时间模型非参数推理。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-01-01 Epub Date: 2022-01-12 DOI: 10.1007/s10985-021-09541-5

Mihai C Giurcanu, Theodore G Karrison

{"title":"Nonparametric inference in the accelerated failure time model using restricted means.","authors":"Mihai C Giurcanu, Theodore G Karrison","doi":"10.1007/s10985-021-09541-5","DOIUrl":"https://doi.org/10.1007/s10985-021-09541-5","url":null,"abstract":"We propose a nonparametric estimate of the scale-change parameter for characterizing the difference between two survival functions under the accelerated failure time model using an estimating equation based on restricted means. Advantages of our restricted means based approach compared to current nonparametric procedures is the strictly monotone nature of the estimating equation as a function of the scale-change parameter, leading to a unique root, as well as the availability of a direct standard error estimate, avoiding the need for hazard function estimation or re-sampling to conduct inference. We derive the asymptotic properties of the proposed estimator for fixed and for random point of restriction. In a simulation study, we compare the performance of the proposed estimator with parametric and nonparametric competitors in terms of bias, efficiency, and accuracy of coverage probabilities. The restricted means based approach provides unbiased estimates and accurate confidence interval coverage rates with efficiency ranging from 81% to 95% relative to fitting the correct parametric model. An example from a randomized clinical trial in head and neck cancer is provided to illustrate an application of the methodology in practice.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 1","pages":"23-39"},"PeriodicalIF":1.3,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39902249","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

Bayesian analysis under accelerated failure time models with error-prone time-to-event outcomes. 具有易出错时间到事件结果的加速失效时间模型下的贝叶斯分析。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-01-01 Epub Date: 2022-01-09 DOI: 10.1007/s10985-021-09543-3

Yanlin Tang, Xinyuan Song, Grace Yun Yi

引用次数: 2

Semiparametric analysis of multivariate panel count data with nonlinear interactions. 具有非线性相互作用的多变量面板计数数据的半参数分析。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-01-01 Epub Date: 2021-10-05 DOI: 10.1007/s10985-021-09537-1

Weiwei Wang, Yijun Wang, Xiaobing Zhao

引用次数: 1

Sample size calculation for clustered survival data under subunit randomization. 亚单位随机化下聚类生存数据的样本量计算。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-01-01 Epub Date: 2021-10-29 DOI: 10.1007/s10985-021-09538-0

Jianghao Li, Sin-Ho Jung

引用次数: 0

An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data. 具有聚类和截尾数据的加速失效时间模型的有效gehan型估计。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2021-10-01 Epub Date: 2021-07-02 DOI: 10.1007/s10985-021-09526-4

Liya Fu, Zhuoran Yang, Yan Zhou, You-Gan Wang

{"title":"An efficient Gehan-type estimation for the accelerated failure time model with clustered and censored data.","authors":"Liya Fu, Zhuoran Yang, Yan Zhou, You-Gan Wang","doi":"10.1007/s10985-021-09526-4","DOIUrl":"https://doi.org/10.1007/s10985-021-09526-4","url":null,"abstract":"In medical studies, the collected covariates contain underlying outliers. For clustered/longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data. We provide the asymptotic properties of the resulting estimators and carry out simulation studies to evaluate the performance of the proposed method under a variety of realistic settings. The simulation results demonstrate that the proposed method is robust to the outliers existing in the covariate domain and lead to much more efficient estimators when a strong within-cluster correlation exists. Finally, the proposed method is applied to two medical datasets and more reliable and convincing results are hence obtained.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"27 4","pages":"679-709"},"PeriodicalIF":1.3,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1007/s10985-021-09526-4","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39064752","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: 3