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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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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
Journal
STATISTICAL METHODS IN MEDICAL RESEARCH
DOI
https://doi.org/10.1177/0962280215615003
Collections
Department of Statistics, Article
Citation Formats
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BibTeX
Z. I. Kalaylıoğlu Akyıldız and H. Demirhan, “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, 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/39314.