title: Bayesian modeling of the dependence in longitudinal data via partial autocorrelations and marginal variances.
abstract: Many parameters and positive-definiteness are two major obstacles in estimating and modelling a correlation matrix for longitudinal data. In addition, when longitudinal data is incomplete, incorrectly modelling the correlation matrix often results in bias in estimating mean regression parameters. In this paper, we introduce a flexible and parsimonious class of regression models for a covariance matrix parameterized using marginal variances and partial autocorrelations. The partial autocorrelations can freely vary in the interval (-1, 1) while maintaining positive definiteness of the correlation matrix so the regression parameters in these models will have no constraints. We propose a class of priors for the regression coefficients and examine the importance of correctly modeling the correlation structure on estimation of longitudinal (mean) trajectories and the performance of the DIC in choosing the correct correlation model via simulations. The regression approach is illustrated on data from a longitudinal clinical trial.
authors: Department of Biostatistics University of Florida Dauer Hall Gainesville, Florida 32611 yanpin@ufl.edu.
authors: Y
authors: Y
authors: Wang
authors: M J
authors: MJ
authors: Daniels
keywords: Generalized linear model; Markov Chain Monte Carlo; Uniform prior
references: Stat Med. 2003 May 30;22(10):1631-47
references: 12720301
references: Biometrics. 1992 Sep;48(3):733-42
references: 1420837
references: Biometrics. 2002 Mar;58(1):225-31
references: 11890319
references: Biostatistics. 2006 Jan;7(1):1-15
references: 15917373
references: Biometrics. 2001 Dec;57(4):1173-84
references: 11764258
references: Biometrics. 2011 Sep;67(3):810-8
references: 21361893
references: J Multivar Anal. 2009 Nov 1;100(10):2352-2363
references: 20161018
references: Acta Psychiatr Scand Suppl. 1990;358:72-7
references: 1978496