Lifetime Data Analysis最新文献_第10页

Optimum test planning for heterogeneous inverse Gaussian processes 异构逆高斯过程的最优测试规划

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-06-13 DOI: 10.1007/s10985-022-09556-6

Chien‐Yu Peng, H. Nagatsuka, Ya-Shan Cheng

引用次数: 1

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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

Regression analysis of additive hazards model with sparse longitudinal covariates. 纵向稀疏协变量加性危害模型的回归分析。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-04-01 Epub Date: 2022-02-11 DOI: 10.1007/s10985-022-09548-6

Zhuowei Sun, Hongyuan Cao, Li Chen

引用次数: 3

Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients. 针对具有时变系数的特定原因危害建模的可扩展近似方法。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-04-01 Epub Date: 2022-01-29 DOI: 10.1007/s10985-021-09544-2

Wenbo Wu, Jeremy M G Taylor, Andrew F Brouwer, Lingfeng Luo, Jian Kang, Hui Jiang, Kevin He

{"title":"Scalable proximal methods for cause-specific hazard modeling with time-varying coefficients.","authors":"Wenbo Wu, Jeremy M G Taylor, Andrew F Brouwer, Lingfeng Luo, Jian Kang, Hui Jiang, Kevin He","doi":"10.1007/s10985-021-09544-2","DOIUrl":"10.1007/s10985-021-09544-2","url":null,"abstract":"Survival modeling with time-varying coefficients has proven useful in analyzing time-to-event data with one or more distinct failure types. When studying the cause-specific etiology of breast and prostate cancers using the large-scale data from the Surveillance, Epidemiology, and End Results (SEER) Program, we encountered two major challenges that existing methods for estimating time-varying coefficients cannot tackle. First, these methods, dependent on expanding the original data in a repeated measurement format, result in formidable time and memory consumption as the sample size escalates to over one million. In this case, even a well-configured workstation cannot accommodate their implementations. Second, when the large-scale data under analysis include binary predictors with near-zero variance (e.g., only 0.6% of patients in our SEER prostate cancer data had tumors regional to the lymph nodes), existing methods suffer from numerical instability due to ill-conditioned second-order information. The estimation accuracy deteriorates further with multiple competing risks. To address these issues, we propose a proximal Newton algorithm with a shared-memory parallelization scheme and tests of significance and nonproportionality for the time-varying effects. A simulation study shows that our scalable approach reduces the time and memory costs by orders of magnitude and enjoys improved estimation accuracy compared with alternative approaches. Applications to the SEER cancer data demonstrate the real-world performance of the proximal Newton algorithm.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9201734/pdf/nihms-1801776.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39746403","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。