Lifetime Data Analysis最新文献_第9页

Special issue dedicated to David Oakes. 大卫·奥克斯的特刊。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 DOI: 10.1007/s10985-022-09572-6

Jong H Jeong, Amita K Manatunga

引用次数: 0

Semiparametric single-index models for optimal treatment regimens with censored outcomes. 具有审查结果的最佳治疗方案的半参数单指标模型。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 DOI: 10.1007/s10985-022-09566-4

Jin Wang, Donglin Zeng, D Y Lin

{"title":"Semiparametric single-index models for optimal treatment regimens with censored outcomes.","authors":"Jin Wang, Donglin Zeng, D Y Lin","doi":"10.1007/s10985-022-09566-4","DOIUrl":"https://doi.org/10.1007/s10985-022-09566-4","url":null,"abstract":"There is a growing interest in precision medicine, where a potentially censored survival time is often the most important outcome of interest. To discover optimal treatment regimens for such an outcome, we propose a semiparametric proportional hazards model by incorporating the interaction between treatment and a single index of covariates through an unknown monotone link function. This model is flexible enough to allow non-linear treatment-covariate interactions and yet provides a clinically interpretable linear rule for treatment decision. We propose a sieve maximum likelihood estimation approach, under which the baseline hazard function is estimated nonparametrically and the unknown link function is estimated via monotone quadratic B-splines. We show that the resulting estimators are consistent and asymptotically normal with a covariance matrix that attains the semiparametric efficiency bound. The optimal treatment rule follows naturally as a linear combination of the maximum likelihood estimators of the model parameters. Through extensive simulation studies and an application to an AIDS clinical trial, we demonstrate that the treatment rule derived from the single-index model outperforms the treatment rule under the standard Cox proportional hazards model.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 4","pages":"744-763"},"PeriodicalIF":1.3,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10349573/pdf/nihms-1913037.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9775484","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

Assessing dynamic covariate effects with survival data. 用生存数据评估动态协变量效应。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 DOI: 10.1007/s10985-022-09571-7

Ying Cui, Limin Peng

{"title":"Assessing dynamic covariate effects with survival data.","authors":"Ying Cui, Limin Peng","doi":"10.1007/s10985-022-09571-7","DOIUrl":"https://doi.org/10.1007/s10985-022-09571-7","url":null,"abstract":"Dynamic (or varying) covariate effects often manifest meaningful physiological mechanisms underlying chronic diseases. However, a static view of covariate effects is typically adopted by standard approaches to evaluating disease prognostic factors, which can result in depreciation of some important disease markers. To address this issue, in this work, we take the perspective of globally concerned quantile regression, and propose a flexible testing framework suited to assess either constant or dynamic covariate effects. We study the powerful Kolmogorov-Smirnov (K-S) and Cramér-Von Mises (C-V) type test statistics and develop a simple resampling procedure to tackle their complicated limit distributions. We provide rigorous theoretical results, including the limit null distributions and consistency under a general class of alternative hypotheses of the proposed tests, as well as the justifications for the presented resampling procedure. Extensive simulation studies and a real data example demonstrate the utility of the new testing procedures and their advantages over existing approaches in assessing dynamic covariate effects.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 4","pages":"675-699"},"PeriodicalIF":1.3,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901566/pdf/nihms-1867801.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10655787","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: 1

Mixture survival trees for cancer risk classification. 用于癌症风险分类的混合生存树。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-07-01 DOI: 10.1007/s10985-022-09552-w

Beilin Jia, Donglin Zeng, Jason J Z Liao, Guanghan F Liu, Xianming Tan, Guoqing Diao, Joseph G Ibrahim

引用次数: 0

Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes. 最佳个体化治疗规则的隐私保护估计：最大限度延长严重抑郁症相关结果的案例研究。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2022-07-01 Epub Date: 2022-05-02 DOI: 10.1007/s10985-022-09554-8

Erica E M Moodie, Janie Coulombe, Coraline Danieli, Christel Renoux, Susan M Shortreed

{"title":"Privacy-preserving estimation of an optimal individualized treatment rule: a case study in maximizing time to severe depression-related outcomes.","authors":"Erica E M Moodie, Janie Coulombe, Coraline Danieli, Christel Renoux, Susan M Shortreed","doi":"10.1007/s10985-022-09554-8","DOIUrl":"10.1007/s10985-022-09554-8","url":null,"abstract":"Estimating individualized treatment rules-particularly in the context of right-censored outcomes-is challenging because the treatment effect heterogeneity of interest is often small, thus difficult to detect. While this motivates the use of very large datasets such as those from multiple health systems or centres, data privacy may be of concern with participating data centres reluctant to share individual-level data. In this case study on the treatment of depression, we demonstrate an application of distributed regression for privacy protection used in combination with dynamic weighted survival modelling (DWSurv) to estimate an optimal individualized treatment rule whilst obscuring individual-level data. In simulations, we demonstrate the flexibility of this approach to address local treatment practices that may affect confounding, and show that DWSurv retains its double robustness even when performed through a (weighted) distributed regression approach. The work is motivated by, and illustrated with, an analysis of treatment for unipolar depression using the United Kingdom's Clinical Practice Research Datalink.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 3","pages":"512-542"},"PeriodicalIF":1.2,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10805063/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41217922","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

Semi-supervised approach to event time annotation using longitudinal electronic health records. 使用纵向电子健康记录的事件时间注释的半监督方法。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-07-01 DOI: 10.1007/s10985-022-09557-5

Liang Liang, Jue Hou, Hajime Uno, Kelly Cho, Yanyuan Ma, Tianxi Cai

{"title":"Semi-supervised approach to event time annotation using longitudinal electronic health records.","authors":"Liang Liang, Jue Hou, Hajime Uno, Kelly Cho, Yanyuan Ma, Tianxi Cai","doi":"10.1007/s10985-022-09557-5","DOIUrl":"https://doi.org/10.1007/s10985-022-09557-5","url":null,"abstract":"Large clinical datasets derived from insurance claims and electronic health record (EHR) systems are valuable sources for precision medicine research. These datasets can be used to develop models for personalized prediction of risk or treatment response. Efficiently deriving prediction models using real world data, however, faces practical and methodological challenges. Precise information on important clinical outcomes such as time to cancer progression are not readily available in these databases. The true clinical event times typically cannot be approximated well based on simple extracts of billing or procedure codes. Whereas, annotating event times manually is time and resource prohibitive. In this paper, we propose a two-step semi-supervised multi-modal automated time annotation (MATA) method leveraging multi-dimensional longitudinal EHR encounter records. In step I, we employ a functional principal component analysis approach to estimate the underlying intensity functions based on observed point processes from the unlabeled patients. In step II, we fit a penalized proportional odds model to the event time outcomes with features derived in step I in the labeled data where the non-parametric baseline function is approximated using B-splines. Under regularity conditions, the resulting estimator of the feature effect vector is shown as root-n consistent. We demonstrate the superiority of our approach relative to existing approaches through simulations and a real data example on annotating lung cancer recurrence in an EHR cohort of lung cancer patients from Veteran Health Administration.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"28 3","pages":"428-491"},"PeriodicalIF":1.3,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10044535/pdf/nihms-1879201.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9194040","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: 4

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":"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