A Bayesian joint model for compositional mediation effect selection in microbiome data.

Autor: Fu J; Department of Statistics, Rice University, Houston, Texas, USA., Koslovsky MD; Department of Statistics, Colorado State University, Fort Collins, Colorado, USA., Neophytou AM; Department of Environmental & Radiological Health Sciences, Colorado State University, Fort Collins, Colorado, USA., Vannucci M; Department of Statistics, Rice University, Houston, Texas, USA.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2023 Jul 30; Vol. 42 (17), pp. 2999-3015. Date of Electronic Publication: 2023 May 12.
DOI: 10.1002/sim.9764
Abstrakt: Analyzing multivariate count data generated by high-throughput sequencing technology in microbiome research studies is challenging due to the high-dimensional and compositional structure of the data and overdispersion. In practice, researchers are often interested in investigating how the microbiome may mediate the relation between an assigned treatment and an observed phenotypic response. Existing approaches designed for compositional mediation analysis are unable to simultaneously determine the presence of direct effects, relative indirect effects, and overall indirect effects, while quantifying their uncertainty. We propose a formulation of a Bayesian joint model for compositional data that allows for the identification, estimation, and uncertainty quantification of various causal estimands in high-dimensional mediation analysis. We conduct simulation studies and compare our method's mediation effects selection performance with existing methods. Finally, we apply our method to a benchmark data set investigating the sub-therapeutic antibiotic treatment effect on body weight in early-life mice.
(© 2023 The Authors. Statistics in Medicine published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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