Discovering structure in multiple outcomes models for tests of childhood neurodevelopment.
Biometrics
; 76(3): 874-885, 2020 09.
Article
en En
| MEDLINE
| ID: mdl-31729013
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.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Teorema de Bayes
Tipo de estudio:
Prognostic_studies
Límite:
Child
/
Female
/
Humans
/
Pregnancy
Idioma:
En
Revista:
Biometrics
Año:
2020
Tipo del documento:
Article
Pais de publicación:
Estados Unidos