title: An efficient technique for Bayesian modeling of family data using the BUGS software.
abstract: Linear mixed models have become a popular tool to analyze continuous data from family-based designs by using random effects that model the correlation of subjects from the same family. However, mixed models for family data are challenging to implement with the BUGS (Bayesian inference Using Gibbs Sampling) software because of the high-dimensional covariance matrix of the random effects. This paper describes an efficient parameterization that utilizes the singular value decomposition of the covariance matrix of random effects, includes the BUGS code for such implementation, and extends the parameterization to generalized linear mixed models. The implementation is evaluated using simulated data and an example from a large family-based study is presented with a comparison to other existing methods.
authors.affiliation: School of Biological and Population Health Sciences, College of Public Health and Human Sciences, Oregon State University Corvallis, OR, USA.
authors.forename: Harold T
authors.initials: HT
authors.lastname: Bae
authors.affiliation: New England Centenarian Study, Department of Medicine, Boston University School of Medicine Boston, MA, USA.
authors.forename: Thomas T
authors.initials: TT
authors.lastname: Perls
authors.affiliation: Department of Biostatistics, Boston University School of Public Health Boston, MA, USA.
authors.forename: Paola
authors.initials: P
authors.lastname: Sebastiani
keywords: BUGS; covariance matrix; family-based study; linear mixed models; parameterization