Inverse probability weighting-based mediation analysis for microbiome data
7 affiliations
- 1Department of Statistics and Data Science, The University of Texas at San Antonio , San Antonio, TX 78207,
- 2Department of Biostatistics, The University of Texas MD Anderson Cancer Center , Houston, TX 77030,
- 3Statistics for Clinical Development – US, BeOne Medicines , San Carlos, CA 94070,
- 4Department of Veterinary Pathobiology, Texas A&M University , College Station, TX 77843,
- 5Department of Infectious Diseases, Infection Control and Employee Health, The University of Texas MD Anderson Cancer Center , Houston, TX 77030,
- 6Department of Statistical Sciences, University of Toronto , Toronto, ON M5G 1Z5,
- 7Department of Biostatistics, Columbia University , New York, NY 10032,
Abstract
Motivation: Mediation analysis is an important tool for elucidating causal mechanisms in biomedical research and has recently gained increasing attention in microbiome studies. Motivated by a microbiome study of acute myeloid leukemia (AML) patients, we investigate whether the effect of induction chemotherapy (IC) intensity on infection status is mediated through gut microbial taxa. This problem poses substantial methodological challenges due to the high dimensionality, zero inflation, and dependence of microbial mediators, as well as the presence of an exposure-induced mediator–outcome confounder, antibiotic use.
Results: To address these challenges, we propose a novel nonparametric identification formula for the interventional indirect effect, a causal mediation estimand that remains identifiable in the presence of exposure-induced mediator–outcome confounding. Building on this identification result, we develop an inverse probability-weighting estimator and construct standard normal bootstrap confidence intervals for inference. Simulation studies demonstrate that the proposed method achieves accurate effect estimation and maintains appropriate type I error rates and power in finite samples. Application to the AML microbiome study provides evidence of a statistically significant interventional indirect effect of IC intensity on infection risk through alterations in the gut microbiome.
Availability and implementation: The R code for the simulation studies is available at https://github.com/YX-ZHANG-1/CausalMediationMicrobiome and archived on Zenodo at https://doi.org/10.5281/zenodo.21960802
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BibTeX
@article{zhang2026inverse,
title = {{Inverse probability weighting-based mediation analysis for microbiome data}},
author = {Yuexia Zhang and Jian Wang and Jiayi Shen and Jessica Galloway-Peña and Samuel Shelburne and Linbo Wang and Jianhua Hu},
journal = {Bioinformatics},
year = {2026},
month = oct,
doi = {10.1093/bioinformatics/btag750},
url = {https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag750/8913742},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Inverse probability weighting-based mediation analysis for microbiome data
AU - Yuexia Zhang
AU - Jian Wang
AU - Jiayi Shen
AU - Jessica Galloway-Peña
AU - Samuel Shelburne
AU - Linbo Wang
AU - Jianhua Hu
PY - 2026
DA - 2026/10/13
JO - Bioinformatics
DO - 10.1093/bioinformatics/btag750
UR - https://academic.oup.com/bioinformatics/article/doi/10.1093/bioinformatics/btag750/8913742
AB - Motivation: Mediation analysis is an important tool for elucidating causal mechanisms in biomedical research and has recently gained increasing attention in microbiome studies. Motivated by a microbiome study of acute myeloid leukemia (AML) patients, we investigate whether the effect of induction chemotherapy (IC) intensity on infection status is mediated through gut microbial taxa. This problem poses substantial methodological challenges due to the high dimensionality, zero inflation, and dependence of microbial mediators, as well as the presence of an exposure-induced mediator–outcome confounder, antibiotic use. Results: To address these challenges, we propose a novel nonparametric identification formula for the interventional indirect effect, a causal mediation estimand that remains identifiable in the presence of exposure-induced mediator–outcome confounding. Building on this identification result, we develop an inverse probability-weighting estimator and construct standard normal bootstrap confidence intervals for inference. Simulation studies demonstrate that the proposed method achieves accurate effect estimation and maintains appropriate type I error rates and power in finite samples. Application to the AML microbiome study provides evidence of a statistically significant interventional indirect effect of IC intensity on infection risk through alterations in the gut microbiome. Availability and implementation: The R code for the simulation studies is available at https://github.com/YX-ZHANG-1/CausalMediationMicrobiome and archived on Zenodo at https://doi.org/10.5281/zenodo.21960802
SN - 1367-4803
SN - 1367-4811
ER -