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A Bayesian latent variable mixture model for longitudinal fetal growth.

Publication ,  Journal Article
Slaughter, JC; Herring, AH; Thorp, JM
Published in: Biometrics
December 2009

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.

Duke Scholars

Published In

Biometrics

DOI

EISSN

1541-0420

ISSN

0006-341X

Publication Date

December 2009

Volume

65

Issue

4

Start / End Page

1233 / 1242

Related Subject Headings

  • Ultrasonography, Prenatal
  • Statistics & Probability
  • Pregnancy
  • Models, Statistical
  • Models, Biological
  • Male
  • Longitudinal Studies
  • Infant, Newborn
  • Infant, Low Birth Weight
  • Humans
 

Citation

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Slaughter, J. C., Herring, A. H., & Thorp, J. M. (2009). A Bayesian latent variable mixture model for longitudinal fetal growth. Biometrics, 65(4), 1233–1242. https://doi.org/10.1111/j.1541-0420.2009.01188.x
Slaughter, James C., Amy H. Herring, and John M. Thorp. “A Bayesian latent variable mixture model for longitudinal fetal growth.Biometrics 65, no. 4 (December 2009): 1233–42. https://doi.org/10.1111/j.1541-0420.2009.01188.x.
Slaughter JC, Herring AH, Thorp JM. A Bayesian latent variable mixture model for longitudinal fetal growth. Biometrics. 2009 Dec;65(4):1233–42.
Slaughter, James C., et al. “A Bayesian latent variable mixture model for longitudinal fetal growth.Biometrics, vol. 65, no. 4, Dec. 2009, pp. 1233–42. Epmc, doi:10.1111/j.1541-0420.2009.01188.x.
Slaughter JC, Herring AH, Thorp JM. A Bayesian latent variable mixture model for longitudinal fetal growth. Biometrics. 2009 Dec;65(4):1233–1242.
Journal cover image

Published In

Biometrics

DOI

EISSN

1541-0420

ISSN

0006-341X

Publication Date

December 2009

Volume

65

Issue

4

Start / End Page

1233 / 1242

Related Subject Headings

  • Ultrasonography, Prenatal
  • Statistics & Probability
  • Pregnancy
  • Models, Statistical
  • Models, Biological
  • Male
  • Longitudinal Studies
  • Infant, Newborn
  • Infant, Low Birth Weight
  • Humans