A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements

Kalaylioglu, Z and Demirhan, H 2017, 'A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements', Statistical Methods in Medical Research, vol. 26, no. 6, pp. 2885-2896.


Document type: Journal Article
Collection: Journal Articles

Title A joint Bayesian approach for the analysis of response measured at a primary endpoint and longitudinal measurements
Author(s) Kalaylioglu, Z
Demirhan, H
Year 2017
Journal name Statistical Methods in Medical Research
Volume number 26
Issue number 6
Start page 2885
End page 2896
Total pages 12
Publisher Sage Publications
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.
Subject Applied Statistics
Biostatistics
Statistical Theory
Keyword(s) Multivariate log gamma distribution
random effects
variance components
variance prior
longitudinal data
DOI - identifier 10.1177/0962280215615003
Copyright notice © The Author(s) 2015
ISSN 0962-2802
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