Multivariate bernoulli mixture models with application to postmortem tissue studies in schizophrenia.
A novel mixture model is presented for repeated measurements in which correlation among repeated observations on the same subject is induced via correlated unobservable component indicators. The mixture components in our model are linear regressions, and the mixing proportions are logits with random effects. Inference is facilitated by sampling from the posterior distribution of the parameters via Markov chain Monte Carlo methods. The model is applied to a neuronal postmortem brain tissue study to examine the differences in neuron volumes between schizophrenic and control subjects.
Sun
Zhuoxin
Z
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, Massachusetts 02115, USA. zhuoxin@jimmy.harvard.edu
Rosen
Ori
O
Sampson
Allan R
AR
D000465:Algorithms; D001344:Autopsy; D001699:Biometry / Q000379:methods; D001921:Brain / Q000473:pathology*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003933:Diagnosis; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D016013:Likelihood Functions; D008959:Models, Neurological; D015233:Models, Statistical; D015999:Multivariate Analysis; D012559:Schizophrenia / Q000473:pathology*; D016008:Statistical Distributions