A Bayesian multivariate mixture model for skewed longitudinal data with intermittent missing observations: An application to infant motor development.

ABSTRACT: 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-Gam

SUBMITTER: Allen C 

PROVIDER: S-EPMC8297425 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature