2015/02/13 by Susan R Bryan, S. R. Bryan, P. H. C. Eilers +18
Engineering · Mathematics · Medicine · #Applications (stat.AP) #FOS: Computer and information sciences #Glaucoma and retinal disorders #Retinal Imaging and Analysis #Traffic and Road Safety #stat.AP
paper · pdf · doi:10.48550/arxiv.1502.03979
29 pages, 2 tables, 6 figures
arxiv created 2015/02/13 · openalex publication_date 2015/02/13 · arxiv updated 2015/02/16 · openalex created_date 2019/06/27 · openalex updated_date 2026/07/28
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