Your browser doesn't support javascript.
loading
A Bayesian multivariate mixture model for skewed longitudinal data with intermittent missing observations: An application to infant motor development.
Allen, Carter; Benjamin-Neelon, Sara E; Neelon, Brian.
Afiliação
  • Allen C; Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio.
  • Benjamin-Neelon SE; Department of Health, Behavior and Society, Johns Hopkins University, Baltimore, Maryland.
  • Neelon B; Department of Public Health Sciences, Medical University of South Carolina, Charleston, South Carolina.
Biometrics ; 77(2): 675-688, 2021 06.
Article em En | MEDLINE | ID: mdl-34305152
In studies of infant growth, an important research goal is to identify latent clusters of infants with delayed motor development-a risk factor for adverse outcomes later in life. However, there are numerous statistical challenges in modeling motor development: the data are typically skewed, exhibit intermittent missingness, and are correlated across repeated measurements over time. Using data from the Nurture study, a cohort of approximately 600 mother-infant pairs, we develop a flexible Bayesian mixture model for the analysis of infant motor development. First, we model developmental trajectories using matrix skew-normal distributions with cluster-specific parameters to accommodate dependence and skewness in the data. Second, we model the cluster-membership probabilities using a Pólya-Gamma data-augmentation scheme, which improves predictions of the cluster-membership allocations. Lastly, we impute missing responses from conditional multivariate skew-normal distributions. Bayesian inference is achieved through straightforward Gibbs sampling. Through simulation studies, we show that the proposed model yields improved inferences over models that ignore skewness or adopt conventional imputation methods. We applied the model to the Nurture data and identified two distinct developmental clusters, as well as detrimental effects of food insecurity on motor development. These findings can aid investigators in targeting interventions during this critical early-life developmental window.
Assuntos
Palavras-chave

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecções por HIV / Modelos Estatísticos Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Infant Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Infecções por HIV / Modelos Estatísticos Tipo de estudo: Observational_studies / Prognostic_studies / Risk_factors_studies Limite: Humans / Infant Idioma: En Ano de publicação: 2021 Tipo de documento: Article