A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease
| dc.citation.articleNumber | e1008473 | en_US |
| dc.citation.issueNumber | 12 | en_US |
| dc.citation.journalTitle | PLoS Computational Biology | en_US |
| dc.citation.volumeNumber | 16 | en_US |
| dc.contributor.author | Luna, Pamela N. | en_US |
| dc.contributor.author | Mansbach, Jonathan M. | en_US |
| dc.contributor.author | Shaw, Chad A. | en_US |
| dc.date.accessioned | 2021-02-08T18:37:56Z | en_US |
| dc.date.available | 2021-02-08T18:37:56Z | en_US |
| dc.date.issued | 2020 | en_US |
| dc.description.abstract | Changes in the composition of the microbiome over time are associated with myriad human illnesses. Unfortunately, the lack of analytic techniques has hindered researchers’ ability to quantify the association between longitudinal microbial composition and time-to-event outcomes. Prior methodological work developed the joint model for longitudinal and time-to-event data to incorporate time-dependent biomarker covariates into the hazard regression approach to disease outcomes. The original implementation of this joint modeling approach employed a linear mixed effects model to represent the time-dependent covariates. However, when the distribution of the time-dependent covariate is non-Gaussian, as is the case with microbial abundances, researchers require different statistical methodology. We present a joint modeling framework that uses a negative binomial mixed effects model to determine longitudinal taxon abundances. We incorporate these modeled microbial abundances into a hazard function with a parameterization that not only accounts for the proportional nature of microbiome data, but also generates biologically interpretable results. Herein we demonstrate the performance improvements of our approach over existing alternatives via simulation as well as a previously published longitudinal dataset studying the microbiome during pregnancy. The results demonstrate that our joint modeling framework for longitudinal microbiome count data provides a powerful methodology to uncover associations between changes in microbial abundances over time and the onset of disease. This method offers the potential to equip researchers with a deeper understanding of the associations between longitudinal microbial composition changes and disease outcomes. This new approach could potentially lead to new diagnostic biomarkers or inform clinical interventions to help prevent or treat disease. | en_US |
| dc.identifier.citation | Luna, Pamela N., Mansbach, Jonathan M. and Shaw, Chad A.. "A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease." <i>PLoS Computational Biology,</i> 16, no. 12 (2020) Public Library of Science: https://doi.org/10.1371/journal.pcbi.1008473. | en_US |
| dc.identifier.digital | journal-pcbi-1008473 | en_US |
| dc.identifier.doi | https://doi.org/10.1371/journal.pcbi.1008473 | en_US |
| dc.identifier.uri | https://hdl.handle.net/1911/109820 | en_US |
| dc.language.iso | eng | en_US |
| dc.publisher | Public Library of Science | en_US |
| dc.rights | This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. | en_US |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.title | A joint modeling approach for longitudinal microbiome data improves ability to detect microbiome associations with disease | en_US |
| dc.type | Journal article | en_US |
| dc.type.dcmi | Text | en_US |
| dc.type.publication | publisher version | en_US |
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