Lifetime Data Analysis最新文献_第2页

Nested case-control sampling without replacement. 无替换的嵌套病例对照抽样。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-09-05 DOI: 10.1007/s10985-024-09633-y

Yei Eun Shin, Takumi Saegusa

{"title":"Nested case-control sampling without replacement.","authors":"Yei Eun Shin, Takumi Saegusa","doi":"10.1007/s10985-024-09633-y","DOIUrl":"10.1007/s10985-024-09633-y","url":null,"abstract":"Nested case-control design (NCC) is a cost-effective outcome-dependent design in epidemiology that collects all cases and a fixed number of controls at the time of case diagnosis from a large cohort. Due to inefficiency relative to full cohort studies, previous research developed various estimation methodologies but changing designs in the formulation of risk sets was considered only in view of potential bias in the partial likelihood estimation. In this paper, we study a modified design that excludes previously selected controls from risk sets in view of efficiency improvement as well as bias. To this end, we extend the inverse probability weighting method of Samuelsen which was shown to outperform the partial likelihood estimator in the standard setting. We develop its asymptotic theory and a variance estimation of both regression coefficients and the cumulative baseline hazard function that takes account of the complex feature of the modified sampling design. In addition to good finite sample performance of variance estimation, simulation studies show that the modified design with the proposed estimator is more efficient than the standard design. Examples are provided using data from NIH-AARP Diet and Health Cohort Study.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"776-799"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142134285","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

A flexible time-varying coefficient rate model for panel count data. 面板计数数据的灵活时变系数率模型。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-05-28 DOI: 10.1007/s10985-024-09630-1

Dayu Sun, Yuanyuan Guo, Yang Li, Jianguo Sun, Wanzhu Tu

引用次数: 0

Call for papers for a special issue on survival analysis in artificial intelligence. 人工智能生存分析特刊征稿启事。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-10-16 DOI: 10.1007/s10985-024-09636-9

Xingqiu Zhao

引用次数: 0

Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease. 对终末期肾病的纵向和生存结果进行时空多层次联合建模。

IF 1.2 3区数学

Lifetime Data Analysis Pub Date : 2024-10-01 Epub Date: 2024-10-04 DOI: 10.1007/s10985-024-09635-w

Esra Kürüm, Danh V Nguyen, Qi Qian, Sudipto Banerjee, Connie M Rhee, Damla Şentürk

{"title":"Spatiotemporal multilevel joint modeling of longitudinal and survival outcomes in end-stage kidney disease.","authors":"Esra Kürüm, Danh V Nguyen, Qi Qian, Sudipto Banerjee, Connie M Rhee, Damla Şentürk","doi":"10.1007/s10985-024-09635-w","DOIUrl":"10.1007/s10985-024-09635-w","url":null,"abstract":"Individuals with end-stage kidney disease (ESKD) on dialysis experience high mortality and excessive burden of hospitalizations over time relative to comparable Medicare patient cohorts without kidney failure. A key interest in this population is to understand the time-dynamic effects of multilevel risk factors that contribute to the correlated outcomes of longitudinal hospitalization and mortality. For this we utilize multilevel data from the United States Renal Data System (USRDS), a national database that includes nearly all patients with ESKD, where repeated measurements/hospitalizations over time are nested in patients and patients are nested within (health service) regions across the contiguous U.S. We develop a novel spatiotemporal multilevel joint model (STM-JM) that accounts for the aforementioned hierarchical structure of the data while considering the spatiotemporal variations in both outcomes across regions. The proposed STM-JM includes time-varying effects of multilevel (patient- and region-level) risk factors on hospitalization trajectories and mortality and incorporates spatial correlations across the spatial regions via a multivariate conditional autoregressive correlation structure. Efficient estimation and inference are performed via a Bayesian framework, where multilevel varying coefficient functions are targeted via thin-plate splines. The finite sample performance of the proposed method is assessed through simulation studies. An application of the proposed method to the USRDS data highlights significant time-varying effects of patient- and region-level risk factors on hospitalization and mortality and identifies specific time periods on dialysis and spatial locations across the U.S. with elevated hospitalization and mortality risks.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":" ","pages":"827-852"},"PeriodicalIF":1.2,"publicationDate":"2024-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11502599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142376249","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

Unifying mortality forecasting model: an investigation of the COM–Poisson distribution in the GAS model for improved projections 统一死亡率预测模型：为改进预测而对 GAS 模型中 COM-Poisson 分布的研究

IF 1.3 3区数学

Lifetime Data Analysis Pub Date : 2024-09-13 DOI: 10.1007/s10985-024-09634-x

Suryo Adi Rakhmawan, Tahir Mahmood, Nasir Abbas, Muhammad Riaz

{"title":"Unifying mortality forecasting model: an investigation of the COM–Poisson distribution in the GAS model for improved projections","authors":"Suryo Adi Rakhmawan, Tahir Mahmood, Nasir Abbas, Muhammad Riaz","doi":"10.1007/s10985-024-09634-x","DOIUrl":"https://doi.org/10.1007/s10985-024-09634-x","url":null,"abstract":"Forecasting mortality rates is crucial for evaluating life insurance company solvency, especially amid disruptions caused by phenomena like COVID-19. The Lee–Carter model is commonly employed in mortality modelling; however, extensions that can encompass count data with diverse distributions, such as the Generalized Autoregressive Score (GAS) model utilizing the COM–Poisson distribution, exhibit potential for enhancing time-to-event forecasting accuracy. Using mortality data from 29 countries, this research evaluates various distributions and determines that the COM–Poisson model surpasses the Poisson, binomial, and negative binomial distributions in forecasting mortality rates. The one-step forecasting capability of the GAS model offers distinct advantages, while the COM–Poisson distribution demonstrates enhanced flexibility and versatility by accommodating various distributions, including Poisson and negative binomial. Ultimately, the study determines that the COM–Poisson GAS model is an effective instrument for examining time series data on mortality rates, particularly when facing time-varying parameters and non-conventional data distributions.","PeriodicalId":49908,"journal":{"name":"Lifetime Data Analysis","volume":"60 1","pages":""},"PeriodicalIF":1.3,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142220139","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

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":" ","pages":"624-648"},"PeriodicalIF":1.2,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11634939/pdf/","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":"OA","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":" ","pages":"680-699"},"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

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":" ","pages":"549-571"},"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