title: A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements.
abstract: Joint mixed modeling is an attractive approach for the analysis of a scalar response measured at a primary endpoint and longitudinal measurements on a covariate. In the standard Bayesian analysis of these models, measurement error variance and the variance/covariance of random effects are a priori modeled independently. The key point is that these variances cannot be assumed independent given the total variation in a response. This article presents a joint Bayesian analysis in which these variance terms are a priori modeled jointly. Simulations illustrate that analysis with multivariate variance prior in general lead to reduced bias (smaller relative bias) and improved efficiency (smaller interquartile range) in the posterior inference compared with the analysis with independent variance priors.
authors.affiliation: 1 Department of Statistics, Middle East Technical University, Ankara, Türkiye.
authors.forename: Zeynep
authors.initials: Z
authors.lastname: Kalaylioglu
authors.affiliation: 2 Department of Statistics, Hacettepe University, Beytepe, Ankara, Türkiye.
authors.forename: Haydar
authors.initials: H
authors.lastname: Demirhan
mesh_terms: D001499:Bayes Theorem; D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D008137:Longitudinal Studies; D015233:Models, Statistical; D015999:Multivariate Analysis; D007744:Obstetric Labor Complications / Q000097:blood / Q000209:etiology; D011247:Pregnancy; D011254:Pregnancy in Diabetics / Q000097:blood
chemical_list: D006442:Glycated Hemoglobin; C517652:hemoglobin A1c protein, human
keywords: Multivariate log gamma distribution; longitudinal data; random effects; variance components; variance prior