A Bayesian latent variable mixture model for longitudinal fetal growth.

ABSTRACT: Fetal growth restriction is a leading cause of perinatal morbidity and mortality that could be reduced if high-risk infants are identified early in pregnancy. We propose a Bayesian model for aggregating 18 longitudinal ultrasound measurements of fetal size and blood flow into three underlying, continuous latent factors. Our procedure is more flexible than typical latent variable methods in that we relax the normality assumptions by allowing the latent factors to follow finite mixture distributions. Using mixture distributions also permits us to cluster individuals with similar observed characteristics and identify latent classes of subjects who are more likely to be growth or blood flow restricted during pregnancy. We also use our latent variable mixture distribution model to identify a cl

SUBMITTER: Slaughter JC 

PROVIDER: S-EPMC3717393 | biostudies-literature | 2009 Dec

REPOSITORIES: biostudies-literature