Lifetime Data Analysis最新文献_第2页

A flexible time-varying coefficient rate model for panel count data. 面板计数数据的灵活时变系数率模型。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-05-28 DOI: 10.1007/s10985-024-09630-1

Dayu Sun, Yuanyuan Guo, Yang Li, Jianguo Sun, Wanzhu Tu

引用次数: 0

Regression analysis of doubly censored failure time data with ancillary information 带辅助信息的双删失故障时间数据回归分析

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-04-20 DOI: 10.1007/s10985-024-09625-y

Mingyue Du, Xiyuan Gao, Ling Chen

引用次数: 0

Partial-linear single-index transformation models with censored data 有删减数据的部分线性单指数变换模型

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-04-16 DOI: 10.1007/s10985-024-09624-z

Myeonggyun Lee, Andrea B. Troxel, Mengling Liu

{"title":"Partial-linear single-index transformation models with censored data","authors":"Myeonggyun Lee, Andrea B. Troxel, Mengling Liu","doi":"10.1007/s10985-024-09624-z","DOIUrl":"https://doi.org/10.1007/s10985-024-09624-z","url":null,"abstract":"In studies with time-to-event outcomes, multiple, inter-correlated, and time-varying covariates are commonly observed. It is of great interest to model their joint effects by allowing a flexible functional form and to delineate their relative contributions to survival risk. A class of semiparametric transformation (ST) models offers flexible specifications of the intensity function and can be a general framework to accommodate nonlinear covariate effects. In this paper, we propose a partial-linear single-index (PLSI) transformation model that reduces the dimensionality of multiple covariates into a single index and provides interpretable estimates of the covariate effects. We develop an iterative algorithm using the regression spline technique to model the nonparametric single-index function for possibly nonlinear joint effects, followed by nonparametric maximum likelihood estimation. We also propose a nonparametric testing procedure to formally examine the linearity of covariate effects. We conduct Monte Carlo simulation studies to compare the PLSI transformation model with the standard ST model and apply it to NYU Langone Health de-identified electronic health record data on COVID-19 hospitalized patients’ mortality and a Veteran’s Administration lung cancer trial.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574258","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

Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data 从分层体重校准的病例队列数据中推断相对危险度和纯风险的 Cox 模型

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-04-02 DOI: 10.1007/s10985-024-09621-2

{"title":"Cox model inference for relative hazard and pure risk from stratified weight-calibrated case-cohort data","authors":"","doi":"10.1007/s10985-024-09621-2","DOIUrl":"https://doi.org/10.1007/s10985-024-09621-2","url":null,"abstract":"<h3>Abstract</h3> The case-cohort design obtains complete covariate data only on cases and on a random sample (the subcohort) of the entire cohort. Subsequent publications described the use of stratification and weight calibration to increase efficiency of estimates of Cox model log-relative hazards, and there has been some work estimating pure risk. Yet there are few examples of these options in the medical literature, and we could not find programs currently online to analyze these various options. We therefore present a unified approach and R software to facilitate such analyses. We used influence functions adapted to the various design and analysis options together with variance calculations that take the two-phase sampling into account. This work clarifies when the widely used “robust” variance estimate of Barlow (Biometrics 50:1064–1072, 1994) is appropriate. The corresponding R software, CaseCohortCoxSurvival, facilitates analysis with and without stratification and/or weight calibration, for subcohort sampling with or without replacement. We also allow for phase-two data to be missing at random for stratified designs. We provide inference not only for log-relative hazards in the Cox model, but also for cumulative baseline hazards and covariate-specific pure risks. We hope these calculations and software will promote wider use of more efficient and principled design and analysis options for case-cohort studies.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140574106","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

Efficiency of the Breslow estimator in semiparametric transformation models. 半参数变换模型中Breslow估计量的效率。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-04-01 Epub Date: 2023-11-26 DOI: 10.1007/s10985-023-09611-w

Theresa P Devasia, Alexander Tsodikov

引用次数: 0

A Bayesian proportional hazards mixture cure model for interval-censored data. 区间截尾数据的贝叶斯比例风险混合校正模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-04-01 Epub Date: 2023-11-28 DOI: 10.1007/s10985-023-09613-8

Chun Pan, Bo Cai, Xuemei Sui

引用次数: 0

Quantile difference estimation with censoring indicators missing at random. 用随机缺失的普查指标进行量差估计。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-04-01 Epub Date: 2024-01-18 DOI: 10.1007/s10985-023-09614-7

Cui-Juan Kong, Han-Ying Liang

引用次数: 0

Bias reduction for semi-competing risks frailty model with rare events: application to a chronic kidney disease cohort study in South Korea. 具有罕见事件的半竞争风险脆弱性模型的偏倚减少:在韩国慢性肾脏疾病队列研究中的应用

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-04-01 Epub Date: 2023-11-13 DOI: 10.1007/s10985-023-09612-9

Jayoun Kim, Boram Jeong, Il Do Ha, Kook-Hwan Oh, Ji Yong Jung, Jong Cheol Jeong, Donghwan Lee

{"title":"Bias reduction for semi-competing risks frailty model with rare events: application to a chronic kidney disease cohort study in South Korea.","authors":"Jayoun Kim, Boram Jeong, Il Do Ha, Kook-Hwan Oh, Ji Yong Jung, Jong Cheol Jeong, Donghwan Lee","doi":"10.1007/s10985-023-09612-9","DOIUrl":"10.1007/s10985-023-09612-9","url":null,"abstract":"In a semi-competing risks model in which a terminal event censors a non-terminal event but not vice versa, the conventional method can predict clinical outcomes by maximizing likelihood estimation. However, this method can produce unreliable or biased estimators when the number of events in the datasets is small. Specifically, parameter estimates may converge to infinity, or their standard errors can be very large. Moreover, terminal and non-terminal event times may be correlated, which can account for the frailty term. Here, we adapt the penalized likelihood with Firth's correction method for gamma frailty models with semi-competing risks data to reduce the bias caused by rare events. The proposed method is evaluated in terms of relative bias, mean squared error, standard error, and standard deviation compared to the conventional methods through simulation studies. The results of the proposed method are stable and robust even when data contain only a few events with the misspecification of the baseline hazard function. We also illustrate a real example with a multi-centre, patient-based cohort study to identify risk factors for chronic kidney disease progression or adverse clinical outcomes. This study will provide a better understanding of semi-competing risk data in which the number of specific diseases or events of interest is rare.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89720279","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

On variable selection in a semiparametric AFT mixture cure model. 关于半参数 AFT 混合治愈模型中的变量选择。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-04-01 Epub Date: 2024-03-04 DOI: 10.1007/s10985-024-09619-w

Motahareh Parsa, Seyed Mahmood Taghavi-Shahri, Ingrid Van Keilegom

引用次数: 0

The built-in selection bias of hazard ratios formalized using structural causal models. 利用结构性因果模型对危险比的内在选择偏差进行正规化。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-04-01 Epub Date: 2024-02-15 DOI: 10.1007/s10985-024-09617-y

Richard A J Post, Edwin R van den Heuvel, Hein Putter

{"title":"The built-in selection bias of hazard ratios formalized using structural causal models.","authors":"Richard A J Post, Edwin R van den Heuvel, Hein Putter","doi":"10.1007/s10985-024-09617-y","DOIUrl":"10.1007/s10985-024-09617-y","url":null,"abstract":"It is known that the hazard ratio lacks a useful causal interpretation. Even for data from a randomized controlled trial, the hazard ratio suffers from so-called built-in selection bias as, over time, the individuals at risk among the exposed and unexposed are no longer exchangeable. In this paper, we formalize how the expectation of the observed hazard ratio evolves and deviates from the causal effect of interest in the presence of heterogeneity of the hazard rate of unexposed individuals (frailty) and heterogeneity in effect (individual modification). For the case of effect heterogeneity, we define the causal hazard ratio. We show that the expected observed hazard ratio equals the ratio of expectations of the latent variables (frailty and modifier) conditionally on survival in the world with and without exposure, respectively. Examples with gamma, inverse Gaussian and compound Poisson distributed frailty and categorical (harming, beneficial or neutral) distributed effect modifiers are presented for illustration. This set of examples shows that an observed hazard ratio with a particular value can arise for all values of the causal hazard ratio. Therefore, the hazard ratio cannot be used as a measure of the causal effect without making untestable assumptions, stressing the importance of using more appropriate estimands, such as contrasts of the survival probabilities.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11300553/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139736518","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