A Bayesian Partial Membership Model for Multiple Exposures with Uncertain Group Memberships

Alexis E. Zavez (), Emeir M. McSorley, Alison J. Yeates and Sally W. Thurston
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Alexis E. Zavez: University of Rochester
Emeir M. McSorley: Ulster University
Alison J. Yeates: Ulster University
Sally W. Thurston: University of Rochester

Journal of Agricultural, Biological and Environmental Statistics, 2023, vol. 28, issue 3, No 1, 377-400

Abstract: Abstract We present a Bayesian partial membership model that estimates the associations between an outcome, a small number of latent variables, and multiple observed exposures where the number of latent variables is specified a priori. We assign one observed exposure as the sentinel marker for each latent variable. The model allows non-sentinel exposures to have complete membership in one latent group, or partial membership across two or more latent groups. MCMC sampling is used to determine latent group partial memberships for the non-sentinel exposures, and estimate all model parameters. We compare the performance of our model to competing approaches in a simulation study and apply our model to inflammatory marker data measured in a large mother-child cohort of the Seychelles Child Development Study (SCDS). In simulations, our model estimated model parameters with little bias, adequate coverage, and tighter credible intervals compared to competing approaches. Under our partial membership model with two latent groups, SCDS inflammatory marker classifications generally aligned with the scientific literature. Incorporating additional SCDS inflammatory markers and more latent groups produced similar groupings of markers that also aligned with the literature. Associations between covariates and birth weight were similar across latent variable models and were consistent with earlier work in this SCDS cohort. Supplementary materials accompanying this paper appear online.

Keywords: Immune response; Inflammation; Latent variables; Markov chain Monte Carlo; Multiple exposures; Seychelles Child Development Study (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13253-023-00528-3

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