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Activity Number:
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156
- Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
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Type:
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Contributed
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Date/Time:
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Monday, August 8, 2022 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #323554
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Title:
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Estimation of Mediating Effect Using Inverse Probability Weighting Method with a Zero-Inflated Mediator
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Author(s):
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Dongyang Yang* and Wei Xu
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Companies:
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University of Toronto and University of Toronto, Princess Margaret Cancer Centre
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Keywords:
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causal mediation model;
human microbiome;
zero-inflated mediator;
non-parametric algorithm;
inverse probability weighting
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Abstract:
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Methodology for mediation to analyze the cause and effect relationship through mediators has been increasingly popular over the past decades. The human microbiome can contribute to the pathogenesis of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis is not adequate for microbiome data due to the excessive number of zero values and over-dispersion in the sequencing reads, which arise for both biological and sampling reasons. To address these unique challenges brought by the zero-inflated mediator, we develop a novel mediation analysis algorithm under the potential-outcome framework to fill this gap. The proposed nonparametric model estimates the mediation effect of the microbiome by decomposing indirect effects into two components of the zero-inflated distributions. We conduct simulation studies to investigate the performance of the proposed weighting-based approach and some model-based alternatives. The proposed algorithm is implemented in a real human microbiome study of identifying whether some taxa mediate the relationship between LACTIN-V treatment and immune response.
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Authors who are presenting talks have a * after their name.
