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Discovering structure in multiple outcomes models for tests of childhood neurodevelopment

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  • Amy LaLonde
  • Tanzy Love
  • Sally W. Thurston
  • Philip W. Davidson

Abstract

Bayesian model–based clustering provides a powerful and flexible tool that can be incorporated into regression models to better understand the grouping of observations. Using data from the Seychelles Child Development Study, we explore the effect of prenatal methylmercury exposure on 20 neurodevelopmental outcomes measured in 9‐year‐old children. Rather than cluster individual subjects, we cluster the outcomes within a multiple outcomes model. By using information in the data to nest the outcomes into groups called domains, the model more accurately reflects the shared characteristics of neurodevelopmental domains and improves estimation of the overall and outcome‐specific exposure effects by shrinking effects within and between domains selected by the data. The Bayesian paradigm allows for sampling from the posterior distribution of the grouping parameters; thus, inference can be made about group membership and their defining characteristics. We avoid the often difficult and highly subjective requirement of a priori identification of the total number of groups by incorporating a Dirichlet process prior to form a fully Bayesian multiple outcomes model.

Suggested Citation

  • Amy LaLonde & Tanzy Love & Sally W. Thurston & Philip W. Davidson, 2020. "Discovering structure in multiple outcomes models for tests of childhood neurodevelopment," Biometrics, The International Biometric Society, vol. 76(3), pages 874-885, September.
    • Handle: RePEc:bla:biomet:v:76:y:2020:i:3:p:874-885
    DOI: 10.1111/biom.13174
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    References listed on IDEAS

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    1. Luo Xiao & Sally W. Thurston & David Ruppert & Tanzy M. T. Love & Philip W. Davidson, 2014. "Bayesian Models for Multiple Outcomes in Domains With Application to the Seychelles Child Development Study," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(505), pages 1-10, March.
    2. D. B. Woodard & T. M. T. Love & S. W. Thurston & D. Ruppert & S. Sathyanarayana & S. H. Swan, 2013. "Latent factor regression models for grouped outcomes," Biometrics, The International Biometric Society, vol. 69(3), pages 785-794, September.
    3. Sally W. Thurston & David Ruppert & Philip W. Davidson, 2009. "Bayesian Models for Multiple Outcomes Nested in Domains," Biometrics, The International Biometric Society, vol. 65(4), pages 1078-1086, December.
    4. Sanchez, Brisa N. & Budtz-Jorgensen, Esben & Ryan, Louise M. & Hu, Howard, 2005. "Structural Equation Models: A Review With Applications to Environmental Epidemiology," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1443-1455, December.
    5. Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
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