Lifetime Data Analysis最新文献_第9页

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":null,"pages":null},"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

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":null,"pages":null},"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

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

Screening for chronic diseases: optimizing lead time through balancing prescribed frequency and individual adherence. 慢性疾病筛查:通过平衡处方频率和个人依从性来优化前置时间。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-06-24 DOI: 10.1007/s10985-022-09563-7

John D Rice, Brent A Johnson, Robert L Strawderman

{"title":"Screening for chronic diseases: optimizing lead time through balancing prescribed frequency and individual adherence.","authors":"John D Rice, Brent A Johnson, Robert L Strawderman","doi":"10.1007/s10985-022-09563-7","DOIUrl":"https://doi.org/10.1007/s10985-022-09563-7","url":null,"abstract":"Screening for chronic diseases, such as cancer, is an important public health priority, but traditionally only the frequency or rate of screening has received attention. In this work, we study the importance of adhering to recommended screening policies and develop new methodology to better optimize screening policies when adherence is imperfect. We consider a progressive disease model with four states (healthy, undetectable preclinical, detectable preclinical, clinical), and overlay this with a stochastic screening-behavior model using the theory of renewal processes that allows us to capture imperfect adherence to screening programs in a transparent way. We show that decreased adherence leads to reduced efficacy of screening programs, quantified here using elements of the lead time distribution (i.e., the time between screening diagnosis and when diagnosis would have occurred clinically in the absence of screening). Under the assumption of an inverse relationship between prescribed screening frequency and individual adherence, we show that the optimal screening frequency generally decreases with increasing levels of non-adherence. We apply this model to an example in breast cancer screening, demonstrating how accounting for imperfect adherence affects the recommended screening frequency.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40334301","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

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":null,"pages":null},"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

Accounting for delayed entry into observational studies and clinical trials: length-biased sampling and restricted mean survival time. 考虑延迟进入观察性研究和临床试验:长度偏倚抽样和限制平均生存时间。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-10-01 Epub Date: 2022-07-01 DOI: 10.1007/s10985-022-09562-8

Mei-Ling Ting Lee, John Lawrence, Yiming Chen, G A Whitmore

{"title":"Accounting for delayed entry into observational studies and clinical trials: length-biased sampling and restricted mean survival time.","authors":"Mei-Ling Ting Lee, John Lawrence, Yiming Chen, G A Whitmore","doi":"10.1007/s10985-022-09562-8","DOIUrl":"https://doi.org/10.1007/s10985-022-09562-8","url":null,"abstract":"Individuals in many observational studies and clinical trials for chronic diseases are enrolled well after onset or diagnosis of their disease. Times to events of interest after enrollment are therefore residual or left-truncated event times. Individuals entering the studies have disease that has advanced to varying extents. Moreover, enrollment usually entails probability sampling of the study population. Finally, event times over a short to moderate time horizon are often of interest in these investigations, rather than more speculative and remote happenings that lie beyond the study period. This research report looks at the issue of delayed entry into these kinds of studies and trials. Time to event for an individual is modelled as a first hitting time of an event threshold by a latent disease process, which is taken to be a Wiener process. It is emphasized that recruitment into these studies often involves length-biased sampling. The requisite mathematics for this kind of sampling and delayed entry are presented, including explicit formulas needed for estimation and inference. Restricted mean survival time (RMST) is taken as the clinically relevant outcome measure. Exact parametric formulas for this measure are derived and presented. The results are extended to settings that involve study covariates using threshold regression methods. Methods adapted for clinical trials are presented. An extensive case illustration for a clinical trial setting is then presented to demonstrate the methods, the interpretation of results, and the harvesting of useful insights. The closing discussion covers a number of important issues and concepts.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40464059","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: 2

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

Model selection among Dimension-Reduced generalized Cox models. 降维广义Cox模型的模型选择。

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2022-07-01 Epub Date: 2022-06-28 DOI: 10.1007/s10985-022-09565-5

Ming-Yueh Huang, Kwun Chuen Gary Chan

{"title":"Model selection among Dimension-Reduced generalized Cox models.","authors":"Ming-Yueh Huang, Kwun Chuen Gary Chan","doi":"10.1007/s10985-022-09565-5","DOIUrl":"https://doi.org/10.1007/s10985-022-09565-5","url":null,"abstract":"Conventional semiparametric hazards regression models rely on the specification of particular model formulations, such as proportional-hazards feature and single-index structures. Instead of checking these modeling assumptions one-by-one, we proposed a class of dimension-reduced generalized Cox models, and then a consistent model selection procedure among this class to select covariates with proportional-hazards feature and a proper model formulation for non-proportional-hazards covariates. In this class, the non-proportional-hazards covariates are treated in a nonparametric manner, and a partial sufficient dimension reduction is introduced to reduce the curse of dimensionality. A semiparametric efficient estimation is proposed to estimate these models. Based on the proposed estimation, we further constructed a cross-validation type criterion to consistently select the correct model among this class. Most importantly, this class of hazards regression models contains the fully nonparametric hazards regression model as the most saturated submodel, and hence no further model diagnosis is required. Overall speaking, this model selection approach is more effective than performing a sequence of conventional model checking. The proposed method is illustrated by simulation studies and a data example.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.3,"publicationDate":"2022-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40406545","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

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":null,"pages":null},"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":null,"pages":null},"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