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Related Concept Videos

Longitudinal Studies01:26

Longitudinal Studies

Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Population size is dynamic, increasing with birth rates and immigration, and decreasing with death rates and emigration. In ideal conditions with unlimited resources, populations can increase exponentially, which plots as a J-shaped growth rate curve of population size against time. This type of curve is characteristic of newly-introduced invasive species, or populations that have suffered catastrophic declines and are rebounding.However, realistic environmental conditions limit the number of...
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Related Experiment Video

Updated: Jun 23, 2026

Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Published on: September 17, 2019

A Bayesian latent variable mixture model for longitudinal fetal growth.

James C Slaughter1, Amy H Herring, John M Thorp

  • 1Vanderbilt School of Medicine, Department of Biostatistics, S-2323 Medical Center North, 1161 21st Avenue South, Nashville, Tennessee 37232-2158, USA. james.c.slaughter@vanderbilt.edu

Biometrics
|May 13, 2009
PubMed
Summary
This summary is machine-generated.

Identifying high-risk pregnancies early is key. A new Bayesian model using ultrasound data helps detect fetal growth restriction by identifying distinct latent classes of impaired fetal growth and blood flow.

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Area of Science:

  • Perinatal medicine
  • Biostatistics
  • Maternal-fetal medicine

Background:

  • Fetal growth restriction (FGR) significantly contributes to perinatal complications.
  • Early identification of high-risk pregnancies is crucial for improving outcomes.
  • Current methods for assessing fetal well-being have limitations.

Purpose of the Study:

  • To develop a flexible Bayesian model for analyzing longitudinal fetal ultrasound data.
  • To identify latent classes of fetal growth and blood flow patterns.
  • To associate these latent classes with birth outcomes and maternal covariates.

Main Methods:

  • A Bayesian latent variable mixture model was employed.
  • The model aggregated 18 longitudinal ultrasound measurements (fetal size and blood flow).
  • Finite mixture distributions were used to relax normality assumptions and enable clustering.

Main Results:

  • The model identified distinct latent classes representing different patterns of fetal growth and blood flow.
  • A specific latent class characterized by increased blood flow restriction and below-average size was identified.
  • This class showed a higher likelihood of birth-based growth restriction.

Conclusions:

  • The proposed Bayesian mixture model effectively identifies at-risk pregnancies for fetal growth restriction.
  • The model provides a flexible approach to analyzing complex longitudinal ultrasound data.
  • Identifying specific latent classes can aid in early detection and management of FGR.