The Bayesian approach has become increasingly popular because it allows to model quite complex models via Markov chain Monte Carlo (MCMC) sampling. However, it is also recognized nowadays that MCMC sampling can become computationally prohibitive when a complex model needs to be fit to a large data set. To overcome this problem, we applied and extended a recently proposed two-stage approach to model a complex hierarchical data structure of glaucoma patients who participate in an ongoing Dutch study. Glaucoma is one of the leading causes of blindness in the world. In order to detect deterioration at an early stage, a model for predicting visual fields (VF) in time is needed. Hence, the true underlying VF progression can be determined, and treatment strategies can then be optimized to prevent further VF loss. Since we were unable to fit these data with the classical one-stage approach upon which the current popular Bayesian software is based, we made use of the two-stage Bayesian approach. The considered hierarchical longitudinal model involves estimating a large number of random effects and deals with censoring and high measurement variability. In addition, we extended the approach with tools for model evaluation
Combining longitudinal cohort studies to examine cardiovascular risk factor trajectories across the adult lifespan
University of Florida（佛罗里达大学）；Northwestern University Feinberg School of Medicine（西北大学芬伯格医学院）；Boston University Chobanian & Avedisian School of Medicine（波士顿大学乔巴尼安和阿维迪西安医学院）
A novel extrapolation method is proposed for longitudinal forecasting. A hierarchical Gaussian process model is used to combine nonlinear population change and individual memory of the past to make prediction. The prediction error is minimized through the hierarchical design. The method is further extended to joint modeling of continuous measurements and survival events. The baseline hazard, covariate and joint effects are conveniently modeled in this hierarchical structure. The estimation and inference are implemented in fully Bayesian framework using the objective and shrinkage priors. In si…
DTVEM-RE: A Hierarchical Random-Effects Extension of the Differential Time-Varying Effect Model for Person-Specific Multi-Lag Estimation in Intensive Longitudinal Data
Geisel School of Medicine, Dartmouth College（达特茅斯学院盖泽尔医学院）
Efficient Bayesian inference for non-linear association structures in joint models: A hierarchical approach via INLA
Bordeaux Population Health Research Center, Inserm U1219, University of Bordeaux（波尔多公共卫生研究中心，Inserm U1219，波尔多大学）；King Abdullah University of Science and Technology (KAUST)（阿卜杜拉国王科技大学）
Evolutionary Structural Shift in Security Screening Sensitivity within the U.S. Aviation Network: A 15-Year Longitudinal Bayesian Assessment (2010-2024)
A Bayesian Hierarchical Model for Prediction of Latent Health States from Multiple Data Sources with Application to Active Surveillance of Prostate Cancer
Bayesian joint modelling of longitudinal biomarkers to enable extrapolation of overall survival: an application using larotrectinib trial clinical data
Visible Analytics；Bayer Pharmaceuticals（拜耳制药）；University Medical Centre Hamburg Eppendorf（汉堡埃普多夫大学医学中心）；University Cancer Institute and the Department of Oncology, University Hospital Fundacion Jimenez Diaz, Autonomous University（胡安·希梅内斯·迪亚兹基金会大学医院肿瘤科及癌症研究所，自治大学）；Institut Bergonié（贝尔戈涅研究所）；Gustave Roussy Comprehensive Cancer Center（古斯塔夫·鲁西综合癌症中心）；Department of Statistics, University of Warwick（华威大学统计系）
Predicting Depressive Symptom Severity through Individuals' Nearby Bluetooth Devices Count Data Collected by Mobile Phones: A Preliminary Longitudinal Study
King's College London（伦敦国王学院）；University College London（伦敦大学学院）；South London and Maudsley National Health Services Foundation Trust（南伦敦和莫兹利国家医疗服务基金会信托）；Amsterdam University Medical Centre（阿姆斯特丹大学医学中心）；Vrije Universiteit（自由大学）；Parc Sanitari Sant Joan de Déu（圣琼德杜保健公园）；Universitat de Barcelona（巴塞罗那大学）；Katholieke Universiteit Leuven（鲁汶大学）；LAB University of Applied Sciences（LAB应用科学大学）；Northwestern University（西北大学）；Janssen Research and Development LLC（杨森研发有限责任公司）；H. Lundbeck A/S
brolgar: An R package to BRowse Over Longitudinal Data Graphically and Analytically in R
纵向（面板）数据提供了检查个人的时间模式的机会，因为测量是在不同的，往往是不规则的，时间点收集同一个人。数据通常使用“意大利面条图”可视化，其中为每个个体绘制线图。当覆盖在一个图中时，它可以具有一碗意大利面条的外观。即使是少量的主题，这些情节也太过超载，不容易阅读。个体差异的有趣方面在噪音中消失了。纵向数据通常使用分层线性模型建模，以捕获总体趋势和个体之间的变化，同时考虑各种依赖程度。然而，这些模型可能难以拟合，并且可能会错过不寻常的个体模式。更好的视觉工具可以帮助诊断纵向模型，并更好地捕捉个人经验。本文介绍了R软件包，brolgar（BRowse over纵向数据Graphically and Analytically in R），它提供了识别和总结纵向数据中有趣的个体模式的工具。
Comparative analysis of unsupervised clustering techniques using validation metrics: Study on cognitive features from the Canadian Longitudinal Study on Aging (CLSA)
