Lifetime Data Analysis最新文献_第10页

Estimation and inference of predictive discrimination for survival outcome risk prediction models. 生存结果风险预测模型预测判别的估算和推理。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-04-01 Epub Date: 2022-01-21 DOI: 10.1007/s10985-022-09545-9

Ruosha Li, Jing Ning, Ziding Feng

{"title":"Estimation and inference of predictive discrimination for survival outcome risk prediction models.","authors":"Ruosha Li, Jing Ning, Ziding Feng","doi":"10.1007/s10985-022-09545-9","DOIUrl":"10.1007/s10985-022-09545-9","url":null,"abstract":"Accurate risk prediction has been the central goal in many studies of survival outcomes. In the presence of multiple risk factors, a censored regression model can be employed to estimate a risk prediction rule. Before the prediction tool can be popularized for practical use, it is crucial to rigorously assess its prediction performance. In our motivating example, researchers are interested in developing and validating a risk prediction tool to identify future lung cancer cases by integrating demographic information, disease characteristics and smoking-related data. Considering the long latency period of cancer, it is desirable for a prediction tool to achieve discriminative performance that does not weaken over time. We propose estimation and inferential procedures to comprehensively assess both the overall predictive discrimination and the temporal pattern of an estimated prediction rule. The proposed methods readily accommodate commonly used censored regression models, including the Cox proportional hazards model and the accelerated failure time model. The estimators are consistent and asymptotically normal, and reliable variance estimators are also developed. The proposed methods offer an informative tool for inferring time-dependent predictive discrimination, as well as for comparing the discrimination performance between candidate models. Applications of the proposed methods demonstrate enduring performance of the risk prediction tool in the PLCO study and detected decaying performance in a study of liver disease.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 2","pages":"219-240"},"PeriodicalIF":1.3,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10084512/pdf/nihms-1885116.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9349535","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

Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design. 在广义病例队列设计下，采用协变量调整删减权重的竞争风险回归模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2022-04-01 Epub Date: 2022-01-15 DOI: 10.1007/s10985-022-09546-8

Yayun Xu, Soyoung Kim, Mei-Jie Zhang, David Couper, Kwang Woo Ahn

{"title":"Competing risks regression models with covariates-adjusted censoring weight under the generalized case-cohort design.","authors":"Yayun Xu, Soyoung Kim, Mei-Jie Zhang, David Couper, Kwang Woo Ahn","doi":"10.1007/s10985-022-09546-8","DOIUrl":"10.1007/s10985-022-09546-8","url":null,"abstract":"A generalized case-cohort design has been used when measuring exposures is expensive and events are not rare in the full cohort. This design collects expensive exposure information from a (stratified) randomly selected subset from the full cohort, called the subcohort, and a fraction of cases outside the subcohort. For the full cohort study with competing risks, He et al. (Scand J Stat 43:103-122, 2016) studied the non-stratified proportional subdistribution hazards model with covariate-dependent censoring to directly evaluate covariate effects on the cumulative incidence function. In this paper, we propose a stratified proportional subdistribution hazards model with covariate-adjusted censoring weights for competing risks data under the generalized case-cohort design. We consider a general class of weight functions to account for the generalized case-cohort design. Then, we derive the optimal weight function which minimizes the asymptotic variance of parameter estimates within the general class of weight functions. The proposed estimator is shown to be consistent and asymptotically normally distributed. The simulation studies show (i) the proposed estimator with covariate-adjusted weight is unbiased when the censoring distribution depends on covariates; and (ii) the proposed estimator with the optimal weight function gains parameter estimation efficiency. We apply the proposed method to stem cell transplantation and diabetes data sets.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 2","pages":"241-262"},"PeriodicalIF":1.2,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977245/pdf/nihms-1782166.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9280624","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 calibrated Bayesian method for the stratified proportional hazards model with missing covariates. 缺失协变量分层比例风险模型的校正贝叶斯方法。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-04-01 DOI: 10.1007/s10985-021-09542-4

Soyoung Kim, Jae-Kwang Kim, Kwang Woo Ahn

{"title":"A calibrated Bayesian method for the stratified proportional hazards model with missing covariates.","authors":"Soyoung Kim, Jae-Kwang Kim, Kwang Woo Ahn","doi":"10.1007/s10985-021-09542-4","DOIUrl":"https://doi.org/10.1007/s10985-021-09542-4","url":null,"abstract":"Missing covariates are commonly encountered when evaluating covariate effects on survival outcomes. Excluding missing data from the analysis may lead to biased parameter estimation and a misleading conclusion. The inverse probability weighting method is widely used to handle missing covariates. However, obtaining asymptotic variance in frequentist inference is complicated because it involves estimating parameters for propensity scores. In this paper, we propose a new approach based on an approximate Bayesian method without using Taylor expansion to handle missing covariates for survival data. We consider a stratified proportional hazards model so that it can be used for the non-proportional hazards structure. Two cases for missing pattern are studied: a single missing pattern and multiple missing patterns. The proposed estimators are shown to be consistent and asymptotically normal, which matches the frequentist asymptotic properties. Simulation studies show that our proposed estimators are asymptotically unbiased and the credible region obtained from posterior distribution is close to the frequentist confidence interval. The algorithm is straightforward and computationally efficient. We apply the proposed method to a stem cell transplantation data set.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 2","pages":"169-193"},"PeriodicalIF":1.3,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8977246/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9280623","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 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