4.5 Article

A Bayesian Latent Variable Mixture Model for Longitudinal Fetal Growth

Journal

BIOMETRICS
Volume 65, Issue 4, Pages 1233-1242

Publisher

WILEY-BLACKWELL PUBLISHING, INC
DOI: 10.1111/j.1541-0420.2009.01188.x

Keywords

Bayesian methods; Birth weight; Correlated data; Intrauterine growth restriction; Latent variables; Preterm birth; Small for gestational age

Funding

  1. NIH/NICHD [R03HD045780, HD39373]
  2. NIH/NIEHS [T32ES007018, RR00046]
  3. March of Dimes Birth Defects Foundation
  4. Association of Schools of Public Health/Centers for Disease Control and Prevention

Ask authors/readers for more resources

P>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 clinically meaningful latent class of subjects with low birth weight and early gestational age. We then examine the association of latent classes of intrauterine growth restriction with latent classes of birth outcomes as well as observed maternal covariates including fetal gender and maternal race, parity, body mass index, and height. Our methods identified a latent class of subjects who have increased blood flow restriction and below average intrauterine size during pregnancy. These subjects were more likely to be growth restricted at birth than a class of individuals with typical size and blood flow.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.5
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available