Lifetime Data Analysis最新文献_第9页

Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation. 双截尾数据的半参数回归分析及其在潜伏期估计中的应用。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/s10985-022-09567-3

Kin Yau Wong, Qingning Zhou, Tao Hu

{"title":"Semiparametric regression analysis of doubly-censored data with applications to incubation period estimation.","authors":"Kin Yau Wong, Qingning Zhou, Tao Hu","doi":"10.1007/s10985-022-09567-3","DOIUrl":"https://doi.org/10.1007/s10985-022-09567-3","url":null,"abstract":"The incubation period is a key characteristic of an infectious disease. In the outbreak of a novel infectious disease, accurate evaluation of the incubation period distribution is critical for designing effective prevention and control measures . Estimation of the incubation period distribution based on limited information from retrospective inspection of infected cases is highly challenging due to censoring and truncation. In this paper, we consider a semiparametric regression model for the incubation period and propose a sieve maximum likelihood approach for estimation based on the symptom onset time, travel history, and basic demographics of reported cases. The approach properly accounts for the pandemic growth and selection bias in data collection. We also develop an efficient computation method and establish the asymptotic properties of the proposed estimators. We demonstrate the feasibility and advantages of the proposed methods through extensive simulation studies and provide an application to a dataset on the outbreak of COVID-19.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"29 1","pages":"87-114"},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9281361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9211107","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: 3

A uniformisation-driven algorithm for inference-related estimation of a phase-type ageing model. 相位型老化模型推理相关估计的均匀化驱动算法。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/s10985-022-09577-1

Boquan Cheng, Rogemar Mamon

引用次数: 1

Joint modeling of generalized scale-change models for recurrent event and failure time data. 重复事件与失效时间数据的广义尺度变化模型联合建模。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/s10985-022-09573-5

Xiaoyu Wang, Liuquan Sun

引用次数: 0

Flexible two-piece distributions for right censored survival data. 右截尾生存数据的灵活的两件式分布。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/s10985-022-09574-4

Worku B Ewnetu, Irène Gijbels, Anneleen Verhasselt

{"title":"Flexible two-piece distributions for right censored survival data.","authors":"Worku B Ewnetu, Irène Gijbels, Anneleen Verhasselt","doi":"10.1007/s10985-022-09574-4","DOIUrl":"https://doi.org/10.1007/s10985-022-09574-4","url":null,"abstract":"An important complexity in censored data is that only partial information on the variables of interest is observed. In recent years, a large family of asymmetric distributions and maximum likelihood estimation for the parameters in that family has been studied, in the complete data case. In this paper, we exploit the appealing family of quantile-based asymmetric distributions to obtain flexible distributions for modelling right censored survival data. The flexible distributions can be generated using a variety of symmetric distributions and monotonic link functions. The interesting feature of this family is that the location parameter coincides with an index-parameter quantile of the distribution. This family is also suitable to characterize different shapes of the hazard function (constant, increasing, decreasing, bathtub and upside-down bathtub or unimodal shapes). Statistical inference is done for the whole family of distributions. The parameter estimation is carried out by optimizing a non-differentiable likelihood function. The asymptotic properties of the estimators are established. The finite-sample performance of the proposed method and the impact of censorship are investigated via simulations. Finally, the methodology is illustrated on two real data examples (times to weaning in breast-fed data and German Breast Cancer data).","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"29 1","pages":"34-65"},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9196524","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: 1

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