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Hypothesis test of mediation effect in causal mediation model with high-dimensional continuous mediators

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  • Yen-Tsung Huang
  • Wen-Chi Pan

Abstract

type="main" xml:lang="en"> Causal mediation modeling has become a popular approach for studying the effect of an exposure on an outcome through a mediator. However, current methods are not applicable to the setting with a large number of mediators. We propose a testing procedure for mediation effects of high-dimensional continuous mediators. We characterize the marginal mediation effect, the multivariate component-wise mediation effects, and the L 2 norm of the component-wise effects, and develop a Monte-Carlo procedure for evaluating their statistical significance. To accommodate the setting with a large number of mediators and a small sample size, we further propose a transformation model using the spectral decomposition. Under the transformation model, mediation effects can be estimated using a series of regression models with a univariate transformed mediator, and examined by our proposed testing procedure. Extensive simulation studies are conducted to assess the performance of our methods for continuous and dichotomous outcomes. We apply the methods to analyze genomic data investigating the effect of microRNA miR-223 on a dichotomous survival status of patients with glioblastoma multiforme (GBM). We identify nine gene ontology sets with expression values that significantly mediate the effect of miR-223 on GBM survival.

Suggested Citation

  • 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.
  • Handle: RePEc:bla:biomet:v:72:y:2016:i:2:p:402-413
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    Cited by:

    1. Yuanyuan Qu & Aza Azlina Md Kassim, 2023. "The Impact of Economic Policy Uncertainty on Investment in Real Estate Corporations Based on Sustainable Development: The Mediating Role of House Prices," Sustainability, MDPI, vol. 15(21), pages 1-17, October.
    2. Yi Zhao & Lexin Li & Brian S. Caffo, 2021. "Multimodal neuroimaging data integration and pathway analysis," Biometrics, The International Biometric Society, vol. 77(3), pages 879-889, September.
    3. Li, Lexin & Shi, Chengchun & Guo, Tengfei & Jagust, William J., 2022. "Sequential pathway inference for multimodal neuroimaging analysis," LSE Research Online Documents on Economics 111904, London School of Economics and Political Science, LSE Library.
    4. Qi Zhang, 2022. "High-Dimensional Mediation Analysis with Applications to Causal Gene Identification," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 432-451, December.
    5. Yu-Bo Wang & Cuilin Zhang & Zhen Chen, 2021. "Intergenerational Associations Between Maternal Diet and Childhood Adiposity: A Bayesian Regularized Mediation Analysis," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 524-542, December.
    6. Yanyi Song & Xiang Zhou & Jian Kang & Max T. Aung & Min Zhang & Wei Zhao & Belinda L. Needham & Sharon L. R. Kardia & Yongmei Liu & John D. Meeker & Jennifer A. Smith & Bhramar Mukherjee, 2021. "Bayesian sparse mediation analysis with targeted penalization of natural indirect effects," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1391-1412, November.
    7. Shi, Chengchun & Li, Lexin, 2022. "Testing mediation effects using logic of Boolean matrices," LSE Research Online Documents on Economics 108881, London School of Economics and Political Science, LSE Library.
    8. Wen Wei Loh & Beatrijs Moerkerke & Tom Loeys & Stijn Vansteelandt, 2022. "Nonlinear mediation analysis with high‐dimensional mediators whose causal structure is unknown," Biometrics, The International Biometric Society, vol. 78(1), pages 46-59, March.
    9. Haoyu Wei & Hengrui Cai & Chengchun Shi & Rui Song, 2024. "On Efficient Inference of Causal Effects with Multiple Mediators," Papers 2401.05517, arXiv.org.
    10. Yanyi Song & Xiang Zhou & Min Zhang & Wei Zhao & Yongmei Liu & Sharon L. R. Kardia & Ana V. Diez Roux & Belinda L. Needham & Jennifer A. Smith & Bhramar Mukherjee, 2020. "Bayesian shrinkage estimation of high dimensional causal mediation effects in omics studies," Biometrics, The International Biometric Society, vol. 76(3), pages 700-710, September.
    11. 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.
    12. Cai, Xizhen & Zhu, Yeying & Huang, Yuan & Ghosh, Debashis, 2022. "High-dimensional causal mediation analysis based on partial linear structural equation models," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).

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