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Structured Modeling for Post-Mortem Brain Tissue Data

Structured means have been used in studying possible covariate effects on responses, whereas patterned covariances deal with random effects, missing data, and differing study designs. In this article, we develop new multivariate models with patterned means and covariance matrices to deal with special structures of the post-mortem brain tissue data collected in the Conte Center for the Neuroscience of Mental Disorders at the University of Pittsburgh. We obtain maximum likelihood estimates via the method of scoring for these new structured models. One-iteration estimators from a consistent starting point are used to derive the asymptotic distributions. The model fitting algorithms, as well as the asymptotic distributions, are examined using simulated data, and are applied to data from post-mortem tissue studies in schizophrenia.