Lifetime Data Analysis最新文献_第2页

Special issue dedicated to Mitchell H. Gail, M.D. Ph.D. 米切尔-盖尔医学博士特刊

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-06-24 DOI: 10.1007/s10985-024-09631-0

Mei-Ling Ting Lee

引用次数: 0

A constrained maximum likelihood approach to developing well-calibrated models for predicting binary outcomes. 开发校准良好的二元结果预测模型的受限最大似然法。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-05-08 DOI: 10.1007/s10985-024-09628-9

Yaqi Cao, Weidong Ma, Ge Zhao, Anne Marie McCarthy, Jinbo Chen

{"title":"A constrained maximum likelihood approach to developing well-calibrated models for predicting binary outcomes.","authors":"Yaqi Cao, Weidong Ma, Ge Zhao, Anne Marie McCarthy, Jinbo Chen","doi":"10.1007/s10985-024-09628-9","DOIUrl":"10.1007/s10985-024-09628-9","url":null,"abstract":"The added value of candidate predictors for risk modeling is routinely evaluated by comparing the performance of models with or without including candidate predictors. Such comparison is most meaningful when the estimated risk by the two models are both unbiased in the target population. Very often data for candidate predictors are sourced from nonrepresentative convenience samples. Updating the base model using the study data without acknowledging the discrepancy between the underlying distribution of the study data and that in the target population can lead to biased risk estimates and therefore an unfair evaluation of candidate predictors. To address this issue assuming access to a well-calibrated base model, we propose a semiparametric method for model fitting that enforces good calibration. The central idea is to calibrate the fitted model against the base model by enforcing suitable constraints in maximizing the likelihood function. This approach enables unbiased assessment of model improvement offered by candidate predictors without requiring a representative sample from the target population, thus overcoming a significant practical challenge. We study theoretical properties for model parameter estimates, and demonstrate improvement in model calibration via extensive simulation studies. Finally, we apply the proposed method to data extracted from Penn Medicine Biobank to inform the added value of breast density for breast cancer risk assessment in the Caucasian woman population.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140877759","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

Competing risks and multivariate outcomes in epidemiological and clinical trial research. 流行病学和临床试验研究中的竞争风险和多变量结果。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-05-06 DOI: 10.1007/s10985-024-09629-8

R L Prentice

引用次数: 0

A Bayesian quantile joint modeling of multivariate longitudinal and time-to-event data. 多变量纵向和时间到事件数据的贝叶斯量化联合建模。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-03-01 DOI: 10.1007/s10985-024-09622-1

Damitri Kundu, Shekhar Krishnan, Manash Pratim Gogoi, Kiranmoy Das

{"title":"A Bayesian quantile joint modeling of multivariate longitudinal and time-to-event data.","authors":"Damitri Kundu, Shekhar Krishnan, Manash Pratim Gogoi, Kiranmoy Das","doi":"10.1007/s10985-024-09622-1","DOIUrl":"10.1007/s10985-024-09622-1","url":null,"abstract":"Linear mixed models are traditionally used for jointly modeling (multivariate) longitudinal outcomes and event-time(s). However, when the outcomes are non-Gaussian a quantile regression model is more appropriate. In addition, in the presence of some time-varying covariates, it might be of interest to see how the effects of different covariates vary from one quantile level (of outcomes) to the other, and consequently how the event-time changes across different quantiles. For such analyses linear quantile mixed models can be used, and an efficient computational algorithm can be developed. We analyze a dataset from the Acute Lymphocytic Leukemia (ALL) maintenance study conducted by Tata Medical Center, Kolkata. In this study, the patients suffering from ALL were treated with two standard drugs (6MP and MTx) for the first two years, and three biomarkers (e.g. lymphocyte count, neutrophil count and platelet count) were longitudinally measured. After treatment the patients were followed nearly for the next three years, and the relapse-time (if any) for each patient was recorded. For this dataset we develop a Bayesian quantile joint model for the three longitudinal biomarkers and time-to-relapse. We consider an Asymmetric Laplace Distribution (ALD) for each outcome, and exploit the mixture representation of the ALD for developing a Gibbs sampler algorithm to estimate the regression coefficients. Our proposed model allows different quantile levels for different biomarkers, but still simultaneously estimates the regression coefficients corresponding to a particular quantile combination. We infer that a higher lymphocyte count accelerates the chance of a relapse while a higher neutrophil count and a higher platelet count (jointly) reduce it. Also, we infer that across (almost) all quantiles 6MP reduces the lymphocyte count, while MTx increases the neutrophil count. Simulation studies are performed to assess the effectiveness of the proposed approach.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139998108","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 the role of Volterra integral equations in self-consistent, product-limit, inverse probability of censoring weighted, and redistribution-to-the-right estimators for the survival function. 论 Volterra 积分方程在生存函数的自洽、乘积限制、反删减概率加权和向右再分布估计器中的作用。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-03-21 DOI: 10.1007/s10985-024-09623-0

Robert L Strawderman, Benjamin R Baer

{"title":"On the role of Volterra integral equations in self-consistent, product-limit, inverse probability of censoring weighted, and redistribution-to-the-right estimators for the survival function.","authors":"Robert L Strawderman, Benjamin R Baer","doi":"10.1007/s10985-024-09623-0","DOIUrl":"10.1007/s10985-024-09623-0","url":null,"abstract":"This paper reconsiders several results of historical and current importance to nonparametric estimation of the survival distribution for failure in the presence of right-censored observation times, demonstrating in particular how Volterra integral equations help inter-connect the resulting estimators. The paper begins by considering Efron's self-consistency equation, introduced in a seminal 1967 Berkeley symposium paper. Novel insights provided in the current work include the observations that (i) the self-consistency equation leads directly to an anticipating Volterra integral equation whose solution is given by a product-limit estimator for the censoring survival function; (ii) a definition used in this argument immediately establishes the familiar product-limit estimator for the failure survival function; (iii) the usual Volterra integral equation for the product-limit estimator of the failure survival function leads to an immediate and simple proof that it can be represented as an inverse probability of censoring weighted estimator; (iv) a simple identity characterizes the relationship between natural inverse probability of censoring weighted estimators for the survival and distribution functions of failure; (v) the resulting inverse probability of censoring weighted estimators, attributed to a highly influential 1992 paper of Robins and Rotnitzky, were implicitly introduced in Efron's 1967 paper in its development of the redistribution-to-the-right algorithm. All results developed herein allow for ties between failure and/or censored observations.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140186140","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

Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population. 利用目标人群的汇总统计数据，对基于人群的病例对照研究的时间到事件结果进行风险预测。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-05-28 DOI: 10.1007/s10985-024-09626-x

Jiayin Zheng, Li Hsu

{"title":"Risk projection for time-to-event outcome from population-based case-control studies leveraging summary statistics from the target population.","authors":"Jiayin Zheng, Li Hsu","doi":"10.1007/s10985-024-09626-x","DOIUrl":"10.1007/s10985-024-09626-x","url":null,"abstract":"Risk stratification based on prediction models has become increasingly important in preventing and managing chronic diseases. However, due to cost- and time-limitations, not every population can have resources for collecting enough detailed individual-level information on a large number of people to develop risk prediction models. A more practical approach is to use prediction models developed from existing studies and calibrate them with relevant summary-level information of the target population. Many existing studies were conducted under the population-based case-control design. Gail et al. (J Natl Cancer Inst 81:1879-1886, 1989) proposed to combine the odds ratio estimates obtained from case-control data and the disease incidence rates from the target population to obtain the baseline hazard function, and thereby the pure risk for developing diseases. However, the approach requires the risk factor distribution of cases from the case-control studies be same as the target population, which, if violated, may yield biased risk estimation. In this article, we propose two novel weighted estimating equation approaches to calibrate the baseline risk by leveraging the summary information of (some) risk factors in addition to disease-free probabilities from the targeted population. We establish the consistency and asymptotic normality of the proposed estimators. Extensive simulation studies and an application to colorectal cancer studies demonstrate the proposed estimators perform well for bias reduction in finite samples.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":null,"pages":null},"PeriodicalIF":1.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11283322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158740","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

Measurement error models with zero inflation and multiple sources of zeros, with applications to hard zeros. 零膨胀和多源零的测量误差模型，以及对硬零的应用。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-07-01 Epub Date: 2024-05-28 DOI: 10.1007/s10985-024-09627-w

Anindya Bhadra, Rubin Wei, Ruth Keogh, Victor Kipnis, Douglas Midthune, Dennis W Buckman, Ya Su, Ananya Roy Chowdhury, Raymond J Carroll

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