A Bayesian confirmatory factor model for multivariate observations in the form of two-way tables of data.

ABSTRACT: Researchers collected multiple measurements on patients with schizophrenia and their relatives, as well as control subjects and their relatives, to study vulnerability factors for schizophrenics and their near relatives. Observations across individuals from the same family are correlated, and also the multiple outcome measures on the same individuals are correlated. Traditional data analyses model outcomes separately and thus do not provide information about the interrelationships among outcomes. We propose a novel Bayesian family factor model (BFFM), which extends the classical confirmatory factor analysis model to explain the correlations among observed variables using a combination of family-member and outcome factors. Traditional methods for fitting confirmatory factor analysis models,

SUBMITTER: Chen Q 

PROVIDER: S-EPMC5895511 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature