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Testing for Mediation Effect with Application to Human Microbiome Data

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Abstract

Mediation analysis has been commonly used to study the effect of an exposure on an outcome through a mediator. In this paper, we are interested in exploring the mediation mechanism of microbiome, whose special features make the analysis challenging. We consider the isometric logratio transformation of the relative abundance as the mediator variable. Then, we present a de-biased Lasso estimate for the mediator of interest and derive its standard error estimator, which can be used to develop a test procedure for the interested mediation effect. Extensive simulation studies are conducted to assess the performance of our method. We apply the proposed approach to test the mediation effect of human gut microbiome between the dietary fiber intake and body mass index.

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Acknowledgements

The authors would like to thank the Editor, the Associate Editor, and two reviewers for their constructive and insightful comments and suggestions that greatly improved the manuscript. Research reported in this publication was supported by the NIH R21 AG063370 and UL1 TR002345. The content is solely the responsibility of the authors and does not necessarily represent the official view of the NIH.

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Authors and Affiliations

  1. Center for Applied Mathematics, Tianjin University, Tianjin, 300072, China

    Haixiang Zhang

  2. Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, 55905, USA

    Jun Chen

  3. Department of Biostatistics, University of Florida, Gainesville, FL, 32610, USA

    Zhigang Li

  4. Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, 63110, USA

    Lei Liu

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  1. Haixiang Zhang
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  2. Jun Chen
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  3. Zhigang Li
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  4. Lei Liu
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Correspondence to Lei Liu.

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Zhang, H., Chen, J., Li, Z. et al. Testing for Mediation Effect with Application to Human Microbiome Data. Stat Biosci 13, 313–328 (2021). https://doi.org/10.1007/s12561-019-09253-3

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  • DOI: https://doi.org/10.1007/s12561-019-09253-3

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