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Using Cholesky Decomposition to Explore Individual Differences in Longitudinal Relations between Reading Skills
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Bayesian Models for Multiple Outcomes in Domains with Application to the Seychelles Child Development Study.

Luo Xiao1, Sally W Thurston2, David Ruppert3

  • 1Johns Hopkins University, Department of Biostatistics, Baltimore, MD 21205, USA.

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PubMed
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Prenatal methylmercury exposure impacts child development. Our new model reveals outcomes can span multiple domains, improving analysis of central nervous system effects.

Keywords:
Bayesian variable selectionLatent variable modelMarkov chain Monte CarloMethylmercurySparsity

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Area of Science:

  • Developmental toxicology
  • Neuroscience
  • Biostatistics

Background:

  • The Seychelles Child Development Study (SCDS) investigates prenatal methylmercury exposure effects on children's central nervous system (CNS).
  • Child development outcomes are often categorized into domains like cognition, memory, motor, and social behavior.
  • Existing models typically assume each outcome belongs to only one domain, potentially limiting analysis.

Purpose of the Study:

  • To present a novel statistical framework for analyzing developmental outcomes that may belong to multiple domains.
  • To investigate the assignment of outcomes to domains and simultaneously estimate exposure and covariate effects.
  • To improve the power and accuracy of detecting methylmercury's impact on child neurodevelopment.

Main Methods:

  • Developed a Bayesian Markov Chain Monte Carlo (MCMC) model allowing outcomes to be assigned to multiple domains.
  • Each domain has a sentinel outcome, while other outcomes can have multiple domain memberships.
  • Incorporated random subject-specific effects to account for correlations within and across domains.

Main Results:

  • The model successfully determined sparse domain assignments and increased statistical power for detecting effects.
  • Analysis of SCDS data revealed several outcomes were partially assigned to domains different from their original classification.
  • Simulations confirmed the model's effectiveness in identifying domain assignments and exposure effects compared to single-domain models.

Conclusions:

  • The proposed multi-domain framework offers a more nuanced understanding of child development outcomes and exposure effects.
  • Reclassifying outcomes into multiple domains provides valuable scientific insights into the complex nature of neurodevelopmental endpoints.
  • This approach enhances model misspecification checks and improves the reliability of findings in developmental studies.