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

Date

2017-12-01

Author

Kalaylıoğlu Akyıldız, Zeynep Işıl
Demirhan, Haydar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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 Keywords

Multivariate log gamma distribution, Random effects, Variance components, Variance prior, Longitudinal data

URI

https://hdl.handle.net/11511/39314

Collections

Department of Statistics, Article