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Activity Number:
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340
- Novel Methods for Microbiome and Metabolomic Disease
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Biometrics Section
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Abstract #313647
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Title:
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Flexible Dynamic Models for Longitudinal Microbiome Data
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Author(s):
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Soo-Young Kim* and Ollivier Hyrien and David Fredricks and Sujatha Srinivasan
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Companies:
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Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center
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Keywords:
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Dynamic modeling;
splines;
microbiome
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Abstract:
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We propose a flexible framework to analyze longitudinal microbiome data. The proposed approach combines dynamic modeling (e.g., the Generalized Lotka-Volterra model) to capture expected features of bacterial growth together with regression splines to describe the effects of (unmeasured) host factors on bacterial kinetics. The model also includes covariates (e.g., antibiotics), while accounting for intra-subject dependencies using random effects. We design a computationally efficient penalized maximum likelihood estimator. The performances of the approach are evaluated using simulations. An application to longitudinal qPCR microbiome data is presented to illustrate the method.
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Authors who are presenting talks have a * after their name.
