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

Carter Allen, Sara E. Benjamin‐Neelon and Brian Neelon

Biometrics, 2021, vol. 77, issue 2, 675-688

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‐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.

Date: 2021
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