Lifetime Data Analysis最新文献_第6页

Model-based hypothesis tests for the causal mediation of semi-competing risks. 基于模型的半竞争风险因果中介假设检验。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-01-01 Epub Date: 2023-03-22 DOI: 10.1007/s10985-023-09595-7

Yun-Lin Ho, Ju-Sheng Hong, Yen-Tsung Huang

{"title":"Model-based hypothesis tests for the causal mediation of semi-competing risks.","authors":"Yun-Lin Ho, Ju-Sheng Hong, Yen-Tsung Huang","doi":"10.1007/s10985-023-09595-7","DOIUrl":"10.1007/s10985-023-09595-7","url":null,"abstract":"Analyzing the causal mediation of semi-competing risks has become important in medical research. Semi-competing risks refers to a scenario wherein an intermediate event may be censored by a primary event but not vice versa. Causal mediation analyses decompose the effect of an exposure on the primary outcome into an indirect (mediation) effect: an effect mediated through a mediator, and a direct effect: an effect not through the mediator. Here we proposed a model-based testing procedure to examine the indirect effect of the exposure on the primary event through the intermediate event. Under the counterfactual outcome framework, we defined a causal mediation effect using counting process. To assess statistical evidence for the mediation effect, we proposed two tests: an intersection-union test (IUT) and a weighted log-rank test (WLR). The test statistic was developed from a semi-parametric estimator of the mediation effect using a Cox proportional hazards model for the primary event and a series of logistic regression models for the intermediate event. We built a connection between the IUT and WLR. Asymptotic properties of the two tests were derived, and the IUT was determined to be a size [Formula: see text] test and statistically more powerful than the WLR. In numerical simulations, both the model-based IUT and WLR can properly adjust for confounding covariates, and the Type I error rates of the proposed methods are well protected, with the IUT being more powerful than the WLR. Our methods demonstrate the strongly significant effects of hepatitis B or C on the risk of liver cancer mediated through liver cirrhosis incidence in a prospective cohort study. The proposed method is also applicable to surrogate endpoint analyses in clinical trials.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"119-142"},"PeriodicalIF":1.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9529993","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

Preface. 序言

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-01-01 Epub Date: 2023-12-27 DOI: 10.1007/s10985-023-09615-6

Jialiang Li, Stijn Vansteelandt

引用次数: 0

Causal survival analysis under competing risks using longitudinal modified treatment policies. 利用纵向修正治疗政策进行竞争风险下的因果生存分析。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-01-01 Epub Date: 2023-08-24 DOI: 10.1007/s10985-023-09606-7

Iván Díaz, Katherine L Hoffman, Nima S Hejazi

引用次数: 5

Sensitivity Analysis for Observational Studies with Recurrent Events. 复发事件观察研究的敏感性分析

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-01-01 Epub Date: 2023-08-12 DOI: 10.1007/s10985-023-09607-6

Jeffrey Zhang, Dylan S Small

引用次数: 0

Volume under the ROC surface for high-dimensional independent screening with ordinal competing risk outcomes. ROC表面下的体积用于具有顺序竞争风险结果的高维独立筛查。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-10-01 Epub Date: 2023-05-09 DOI: 10.1007/s10985-023-09600-z

Yang Qu, Yu Cheng

引用次数: 0

Improving marginal hazard ratio estimation using quadratic inference functions. 使用二次推理函数改进边际风险比估计。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-10-01 Epub Date: 2023-05-07 DOI: 10.1007/s10985-023-09598-4

Hongkai Liang, Xiaoguang Wang, Yingwei Peng, Yi Niu

{"title":"Improving marginal hazard ratio estimation using quadratic inference functions.","authors":"Hongkai Liang, Xiaoguang Wang, Yingwei Peng, Yi Niu","doi":"10.1007/s10985-023-09598-4","DOIUrl":"10.1007/s10985-023-09598-4","url":null,"abstract":"Clustered and multivariate failure time data are commonly encountered in biomedical studies and a marginal regression approach is often employed to identify the potential risk factors of a failure. We consider a semiparametric marginal Cox proportional hazards model for right-censored survival data with potential correlation. We propose to use a quadratic inference function method based on the generalized method of moments to obtain the optimal hazard ratio estimators. The inverse of the working correlation matrix is represented by the linear combination of basis matrices in the context of the estimating equation. We investigate the asymptotic properties of the regression estimators from the proposed method. The optimality of the hazard ratio estimators is discussed. Our simulation study shows that the estimator from the quadratic inference approach is more efficient than those from existing estimating equation methods whether the working correlation structure is correctly specified or not. Finally, we apply the model and the proposed estimation method to analyze a study of tooth loss and have uncovered new insights that were previously inaccessible using existing methods.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"823-853"},"PeriodicalIF":1.3,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9470989","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 (1972): recollections and reflections. 考克斯（1972）：回忆与思考。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-10-01 Epub Date: 2023-09-15 DOI: 10.1007/s10985-023-09609-4

David Oakes

引用次数: 0

Estimation and testing for clustered interval-censored bivariate survival data with application using the semi-parametric version of the Clayton-Oakes model. 使用Clayton-Oakes模型的半参数版本对聚类区间截尾二变量生存数据的估计和检验及其应用。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2023-10-01 Epub Date: 2023-01-20 DOI: 10.1007/s10985-022-09588-y

Bernard Rosner, Camden Bay, Robert J Glynn, Gui-Shuang Ying, Maureen G Maguire, Mei-Ling Ting Lee

{"title":"Estimation and testing for clustered interval-censored bivariate survival data with application using the semi-parametric version of the Clayton-Oakes model.","authors":"Bernard Rosner, Camden Bay, Robert J Glynn, Gui-Shuang Ying, Maureen G Maguire, Mei-Ling Ting Lee","doi":"10.1007/s10985-022-09588-y","DOIUrl":"10.1007/s10985-022-09588-y","url":null,"abstract":"The Kaplan-Meier estimator is ubiquitously used to estimate survival probabilities for time-to-event data. It is nonparametric, and thus does not require specification of a survival distribution, but it does assume that the risk set at any time t consists of independent observations. This assumption does not hold for data from paired organ systems such as occur in ophthalmology (eyes) or otolaryngology (ears), or for other types of clustered data. In this article, we estimate marginal survival probabilities in the setting of clustered data, and provide confidence limits for these estimates with intra-cluster correlation accounted for by an interval-censored version of the Clayton-Oakes model. We develop a goodness-of-fit test for general bivariate interval-censored data and apply it to the proposed interval-censored version of the Clayton-Oakes model. We also propose a likelihood ratio test for the comparison of survival distributions between two groups in the setting of clustered data under the assumption of a constant between-group hazard ratio. This methodology can be used both for balanced and unbalanced cluster sizes, and also when the cluster size is informative. We compare our test to the ordinary log rank test and the Lin-Wei (LW) test based on the marginal Cox proportional Hazards model with robust standard errors obtained from the sandwich estimator. Simulation results indicate that the ordinary log rank test over-inflates type I error, while the proposed unconditional likelihood ratio test has appropriate type I error and higher power than the LW test. The method is demonstrated in real examples from the Sorbinil Retinopathy Trial, and the Age-Related Macular Degeneration Study. Raw data from these two trials are provided.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"854-887"},"PeriodicalIF":1.2,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10614833/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9879574","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

Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study. 结合发病和流行人群对疾病自然史的评估：在Nun研究中的应用。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2023-10-01 Epub Date: 2023-05-20 DOI: 10.1007/s10985-023-09602-x

Daewoo Pak, Jing Ning, Richard J Kryscio, Yu Shen

{"title":"Evaluation of the natural history of disease by combining incident and prevalent cohorts: application to the Nun Study.","authors":"Daewoo Pak, Jing Ning, Richard J Kryscio, Yu Shen","doi":"10.1007/s10985-023-09602-x","DOIUrl":"10.1007/s10985-023-09602-x","url":null,"abstract":"The Nun study is a well-known longitudinal epidemiology study of aging and dementia that recruited elderly nuns who were not yet diagnosed with dementia (i.e., incident cohort) and who had dementia prior to entry (i.e., prevalent cohort). In such a natural history of disease study, multistate modeling of the combined data from both incident and prevalent cohorts is desirable to improve the efficiency of inference. While important, the multistate modeling approaches for the combined data have been scarcely used in practice because prevalent samples do not provide the exact date of disease onset and do not represent the target population due to left-truncation. In this paper, we demonstrate how to adequately combine both incident and prevalent cohorts to examine risk factors for every possible transition in studying the natural history of dementia. We adapt a four-state nonhomogeneous Markov model to characterize all transitions between different clinical stages, including plausible reversible transitions. The estimating procedure using the combined data leads to efficiency gains for every transition compared to those from the incident cohort data only.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"752-768"},"PeriodicalIF":1.2,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10199741/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9509992","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

Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring. 具有相依截尾的多变量纵向和生存数据的贝叶斯半参数联合模型。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2023-10-01 Epub Date: 2023-08-15 DOI: 10.1007/s10985-023-09608-5

An-Min Tang, Nian-Sheng Tang, Dalei Yu

引用次数: 0