Lifetime Data Analysis最新文献_第10页

A general class of promotion time cure rate models with a new biological interpretation. 一类具有新的生物学解释的促进时间治愈率模型。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/s10985-022-09575-3

Yolanda M Gómez, Diego I Gallardo, Marcelo Bourguignon, Eduardo Bertolli, Vinicius F Calsavara

引用次数: 0

Bayesian Design of Clinical Trials Using Joint Cure Rate Models for Longitudinal and Time-to-Event Data. 使用联合治愈率模型的纵向和事件时间数据的临床试验贝叶斯设计。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/s10985-022-09581-5

Jiawei Xu, Matthew A Psioda, Joseph G Ibrahim

{"title":"Bayesian Design of Clinical Trials Using Joint Cure Rate Models for Longitudinal and Time-to-Event Data.","authors":"Jiawei Xu, Matthew A Psioda, Joseph G Ibrahim","doi":"10.1007/s10985-022-09581-5","DOIUrl":"https://doi.org/10.1007/s10985-022-09581-5","url":null,"abstract":"For clinical trial design and analysis, there has been extensive work related to using joint models for longitudinal and time-to-event data without a cure fraction (i.e., when all patients are at risk for the event of interest), but comparatively little treatment has been given to design and analysis of clinical trials using joint models that incorporate a cure fraction. In this paper, we develop a Bayesian clinical trial design methodology focused on evaluating the treatment's effect on a time-to-event endpoint using a promotion time cure rate model, where the longitudinal process is incorporated into the hazard model for the promotion times. A piecewise linear hazard model for the period after assessment of the longitudinal measure ends is proposed as an alternative to extrapolating the longitudinal trajectory. This may be advantageous in scenarios where the period of time from the end of longitudinal measurements until the end of observation is substantial. Inference for the time-to-event endpoint is based on a novel estimand which combines the treatment's effect on the probability of cure and its effect on the promotion time distribution, mediated by the longitudinal outcome. We propose an approach for sample size determination such that the design has a high power and a well-controlled type I error rate with both operating characteristics defined from a Bayesian perspective. We demonstrate the methodology by designing a breast cancer clinical trial with a primary time-to-event endpoint where longitudinal outcomes are measured periodically during follow up.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"29 1","pages":"213-233"},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10698884","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

Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors. 具有协变量测量误差的高维稀疏加性风险回归的双偏置校正。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 DOI: 10.1007/s10985-022-09568-2

Xiaobo Wang, Jiayu Huang, Guosheng Yin, Jian Huang, Yuanshan Wu

{"title":"Double bias correction for high-dimensional sparse additive hazards regression with covariate measurement errors.","authors":"Xiaobo Wang, Jiayu Huang, Guosheng Yin, Jian Huang, Yuanshan Wu","doi":"10.1007/s10985-022-09568-2","DOIUrl":"https://doi.org/10.1007/s10985-022-09568-2","url":null,"abstract":"We propose an inferential procedure for additive hazards regression with high-dimensional survival data, where the covariates are prone to measurement errors. We develop a double bias correction method by first correcting the bias arising from measurement errors in covariates through an estimating function for the regression parameter. By adopting the convex relaxation technique, a regularized estimator for the regression parameter is obtained by elaborately designing a feasible loss based on the estimating function, which is solved via linear programming. Using the Neyman orthogonality, we propose an asymptotically unbiased estimator which further corrects the bias caused by the convex relaxation and regularization. We derive the convergence rate of the proposed estimator and establish the asymptotic normality for the low-dimensional parameter estimator and the linear combination thereof, accompanied with a consistent estimator for the variance. Numerical experiments are carried out on both simulated and real datasets to demonstrate the promising performance of the proposed double bias correction method.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"29 1","pages":"115-141"},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9196498","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

A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model. 一种灵活的参数方法，用于分析比例危险模型下可能出现左截断的任意删减数据。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 Epub Date: 2022-10-08 DOI: 10.1007/s10985-022-09579-z

Prabhashi W Withana Gamage, Christopher S McMahan, Lianming Wang

{"title":"A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model.","authors":"Prabhashi W Withana Gamage, Christopher S McMahan, Lianming Wang","doi":"10.1007/s10985-022-09579-z","DOIUrl":"10.1007/s10985-022-09579-z","url":null,"abstract":"The proportional hazards (PH) model is, arguably, the most popular model for the analysis of lifetime data arising from epidemiological studies, among many others. In such applications, analysts may be faced with censored outcomes and/or studies which institute enrollment criterion leading to left truncation. Censored outcomes arise when the event of interest is not observed but rather is known relevant to an observation time(s). Left truncated data occur in studies that exclude participants who have experienced the event prior to being enrolled in the study. If not accounted for, both of these features can lead to inaccurate inferences about the population under study. Thus, to overcome this challenge, herein we propose a novel unified PH model that can be used to accommodate both of these features. In particular, our approach can seamlessly analyze exactly observed failure times along with interval-censored observations, while aptly accounting for left truncation. To facilitate model fitting, an expectation-maximization algorithm is developed through the introduction of carefully structured latent random variables. To provide modeling flexibility, a monotone spline representation is used to approximate the cumulative baseline hazard function. The performance of our methodology is evaluated through a simulation study and is further illustrated through the analysis of two motivating data sets; one that involves child mortality in Nigeria and the other prostate cancer.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"29 1","pages":"188-212"},"PeriodicalIF":1.3,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9852023/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10642053","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: 2

A series of two-sample non-parametric tests for quantile residual life time. 量子残差寿命的一系列双样本非参数检验。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2023-01-01 Epub Date: 2023-01-02 DOI: 10.1007/s10985-022-09580-6

Yimeng Liu, Liwen Wu, Gong Tang, Abdus S Wahed

{"title":"A series of two-sample non-parametric tests for quantile residual life time.","authors":"Yimeng Liu, Liwen Wu, Gong Tang, Abdus S Wahed","doi":"10.1007/s10985-022-09580-6","DOIUrl":"10.1007/s10985-022-09580-6","url":null,"abstract":"Quantile residual lifetime (QRL) is of significant interest in many clinical studies as an easily interpretable quantity compared to other summary measures of survival distributions. In cancer or other chronic diseases, treatments are often compared based on the distributions or quantiles of the residual lifetime. Thus a common problem of interest is to test the equality of the QRL between two populations. In this paper, we propose two classes of tests to compare two QRLs; one class is based on the difference between two estimated QRLs, and the other is based on the estimating function of the QRL, where the estimated QRL from one sample is plugged into the QRL-estimating-function of the other sample. We outline the asymptotic properties of these test statistics. Simulation studies demonstrate that the proposed tests produced Type I errors closer to the nominal level and are superior to some existing tests based on both Type I error and power. Our proposed test statistics are also computationally less intensive and more straightforward compared to tests based on the confidence intervals. We applied the proposed methods to a randomized multicenter phase III trial for breast cancer patients.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"29 1","pages":"234-252"},"PeriodicalIF":1.2,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12054101/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10642537","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

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