Lifetime Data Analysis最新文献_第10页

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

Choice of time scale for analysis of recurrent events data. 重复事件数据分析的时间尺度选择。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-08-15 DOI: 10.1007/s10985-022-09569-1

Philip Hougaard

引用次数: 0

Inference for transition probabilities in non-Markov multi-state models. 非马尔可夫多状态模型中转移概率的推断。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-06-28 DOI: 10.1007/s10985-022-09560-w

Per Kragh Andersen, Eva Nina Sparre Wandall, Maja Pohar Perme

引用次数: 1

On the targets of inference with multivariate failure time data. 多变量失效时间数据的推理目标。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-06-21 DOI: 10.1007/s10985-022-09558-4

Ross L Prentice

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

Median regression models for clustered, interval-censored survival data - An application to prostate surgery study. 聚类、间隔剔除生存数据的中位数回归模型——在前列腺手术研究中的应用。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-08-07 DOI: 10.1007/s10985-022-09570-8

Debajyoti Sinha, Piyali Basak, Stuart R Lipsitz

{"title":"Median regression models for clustered, interval-censored survival data - An application to prostate surgery study.","authors":"Debajyoti Sinha, Piyali Basak, Stuart R Lipsitz","doi":"10.1007/s10985-022-09570-8","DOIUrl":"https://doi.org/10.1007/s10985-022-09570-8","url":null,"abstract":"Genitourinary surgeons and oncologists are particularly interested in whether a robotic surgery improves times to Prostate Specific Antigen (PSA) recurrence compared to a non-robotic surgery for removing the cancerous prostate. Time to PSA recurrence is an example of a survival time that is typically interval-censored between two consecutive clinical inspections with opposite test results. In addition, success of medical devices and technologies often depends on factors such as experience and skill level of the medical service providers, thus leading to clustering of these survival times. For analyzing the effects of surgery types and other covariates on median of clustered interval-censored time to post-surgery PSA recurrence, we present three competing novel models and associated frequentist and Bayesian analyses. The first model is based on a transform-both-sides of survival time with Gaussian random effects to account for the within-cluster association. Our second model assumes an approximate marginal Laplace distribution for the transformed log-survival times with a Gaussian copula to accommodate clustering. Our third model is a special case of the second model with Laplace distribution for the marginal log-survival times and Gaussian copula for the within-cluster association. Simulation studies establish the second model to be highly robust against extreme observations while estimating median regression coefficients. We provide a comprehensive comparison among these three competing models based on the model properties and the computational ease of their Frequentist and Bayesian analysis. We also illustrate the practical implementations and uses of these methods via analysis of a simulated clustered interval-censored data-set similar in design to a post-surgery PSA recurrence study.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"723-743"},"PeriodicalIF":1.3,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40605557","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

Marker-dependent observation and carry-forward of internal covariates in Cox regression. Cox回归中标记依赖观察及内协变量的结转。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-06-20 DOI: 10.1007/s10985-022-09561-9

Richard J Cook, Jerald F Lawless, Bingfeng Xie

引用次数: 0

Bias correction via outcome reassignment for cross-sectional data with binary disease outcome. 通过结果重新分配对具有二元疾病结果的横断面数据进行偏倚校正。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-06-24 DOI: 10.1007/s10985-022-09559-3

Mei-Cheng Wang, Yuxin Zhu

{"title":"Bias correction via outcome reassignment for cross-sectional data with binary disease outcome.","authors":"Mei-Cheng Wang, Yuxin Zhu","doi":"10.1007/s10985-022-09559-3","DOIUrl":"https://doi.org/10.1007/s10985-022-09559-3","url":null,"abstract":"Cross-sectionally sampled data with binary disease outcome are commonly analyzed in observational studies to identify the relationship between covariates and disease outcome. A cross-sectional population is defined as a population of living individuals at the sampling or observational time. It is generally understood that binary disease outcome from cross-sectional data contains less information than longitudinally collected time-to-event data, but there is insufficient understanding as to whether bias can possibly exist in cross-sectional data and how the bias is related to the population risk of interest. Wang and Yang (2021) presented the complexity and bias in cross-sectional data with binary disease outcome with detailed analytical explorations into the data structure. As the distribution of the cross-sectional binary outcome is quite different from the population risk distribution, bias can arise when using cross-sectional data analysis to draw inference for population risk. In this paper we argue that the commonly adopted age-specific risk probability is biased for the estimation of population risk and propose an outcome reassignment approach which reassigns a portion of the observed binary outcome, 0 or 1, to the other disease category. A sign test and a semiparametric pseudo-likelihood method are developed for analyzing cross-sectional data using the OR approach. Simulations and an analysis based on Alzheimer's Disease data are presented to illustrate the proposed methods.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"659-674"},"PeriodicalIF":1.3,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40395608","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