Latent space models (LSM) for network data were introduced by Hoff et al. (2002) under the basic assumption that each node of the network has an unknown position in a D-dimensional Euclidean latent space: generally the smaller the distance between two nodes in the latent space, the greater their probability of being connected. In this paper we propose a variational Bayes approach to estimate the intractable posterior of the LSM.
In many cases, different network views on the same set of nodes are available. It can therefore be useful to build a model able to jointly summarise the information given by all the network views. For this purpose, we introduce the latent space joint model (LSJM) that merges the information given by multiple network views assuming that the probability of a node being connected with other nodes in each network view is explained by a unique latent variable. This model is demonstrated on the analysis of two datasets: the excerpt of 50 girls from `Teenage Friends and Lifestyle Study' data at three time points and the Saccharomyces cerevisiae genetic and physical protein-protein interactions.
Joint modelling of recurrent and terminal events with discretely-distributed non-parametric frailty: application on re-hospitalizations and death in heart failure patients
Joint Modelling of Line and Point Data on Metric Graphs
Department of Mathematical Sciences, Norwegian University of Science and Technology（挪威科技大学数学科学系）；School of Mathematics and Maxwell Institute for Mathematical Sciences, University of Edinburgh（爱丁堡大学数学学院和麦克斯韦数学科学研究所）；Institute of Transport Economics (Norway)（挪威交通经济研究所）
Bayesian Quantile-Based Joint Modelling of Repeated Measurement and Time-to-Event data, with an Application to Lung Function Decline and Time to Infection in Patients with Cystic Fibrosis
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）
Joint modelling of time-dependent biomarker variability and time-to-event outcomes, a two-step approach
Erasmus University Medical Center（伊拉斯姆斯大学医学中心）；European Organisation for Research and Treatment of Cancer（欧洲癌症研究与治疗组织）；Julius Center for Health Sciences and Primary Care, University Medical Center UtrechtUtrecht University（乌得勒支大学医学中心朱利叶斯健康科学与初级保健中心）
Bayesian joint modelling using semiparametric accelerated failure time approaches
The University of Queensland’s Clinical Trials Capability (ULTRA)（昆士兰大学临床试验能力中心（ULTRA））；NHMRC Clinical Trial Centre, University of Sydney（悉尼大学国家健康与医学研究委员会临床试验中心）；Australian and New Zealand Urogenital and Prostate (ANZUP) Cancer Trials Group（澳大利亚和新西兰泌尿生殖和前列腺癌症试验组）
On a penalised likelihood approach for joint modelling of longitudinal covariates and partly interval-censored data -- an application to the Anti-PD1 brain collaboration trial
