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Testing for Mediation Effect with Application to Human Microbiome Data

Author

Listed:
  • Haixiang Zhang

    (Tianjin University)

  • Jun Chen

    (Mayo Clinic)

  • Zhigang Li

    (University of Florida)

  • Lei Liu

    (Washington University in St. Louis)

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.

Suggested Citation

  • Haixiang Zhang & Jun Chen & Zhigang Li & Lei Liu, 2021. "Testing for Mediation Effect with Application to Human Microbiome Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(2), pages 313-328, July.
    • Handle: RePEc:spr:stabio:v:13:y:2021:i:2:d:10.1007_s12561-019-09253-3
    DOI: 10.1007/s12561-019-09253-3
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    References listed on IDEAS

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    1. Haitao Chai & Hongmei Jiang & Lu Lin & Lei Liu, 2018. "A marginalized two-part Beta regression model for microbiome compositional data," PLOS Computational Biology, Public Library of Science, vol. 14(7), pages 1-16, July.
    2. K. Hron & P. Filzmoser & K. Thompson, 2012. "Linear regression with compositional explanatory variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(5), pages 1115-1128, November.
    3. Joanna Morais & Christine Thomas-Agnan & Michel Simioni, 2018. "Using compositional and Dirichlet models for market share regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 45(9), pages 1670-1689, July.
    4. Yen-Tsung Huang & Wen-Chi Pan, 2016. "Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators," Biometrics, The International Biometric Society, vol. 72(2), pages 402-413, June.
    5. Wei Lin & Pixu Shi & Rui Feng & Hongzhe Li, 2014. "Variable selection in regression with compositional covariates," Biometrika, Biometrika Trust, vol. 101(4), pages 785-797.
    6. Jianqing Fan & Jinchi Lv, 2008. "Sure independence screening for ultrahigh dimensional feature space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 849-911, November.
    7. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    8. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    9. Tao Wang & Hongyu Zhao, 2017. "Constructing Predictive Microbial Signatures at Multiple Taxonomic Levels," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 1022-1031, July.
    10. Cun-Hui Zhang & Stephanie S. Zhang, 2014. "Confidence intervals for low dimensional parameters in high dimensional linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 76(1), pages 217-242, January.
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