基于逆概率加权的微生物组数据中介分析
Inverse Probability Weighting-based Mediation Analysis for Microbiome Data
AI总结：
针对微生物组数据高维、零膨胀等特性及抗生素混杂问题，提出基于逆概率加权的非参数干预性间接效应识别与估计算法，经模拟验证性能良好，且发现AML患者化疗强度对感染的影响主要由肠道微生物组介导。
摘要：
中介分析是生物医学及其他科学领域研究因果关联的重要工具，近年来在微生物组研究中受到关注。我们依托一项针对急性髓系白血病（AML）患者的微生物组研究，探究诱导化疗强度水平对感染状态的影响是否由微生物类群丰度介导。微生物中介变量的独特特征——高维度、零膨胀、存在依赖性——要求中介分析发展新的方法学。暴露诱导的中介-结局混杂因素（抗生素使用）的存在，进一步要求分析过程需进行精细处理。为应对我们这项具有启发性的AML微生物组研究中的这些独特挑战，我们针对干预性间接效应（IIE，一种新近开发的中介效应评估指标）提出了一种全新的非参数识别公式，并利用逆概率加权法开发了相应的估计算法，还通过构建标准正态自助法置信区间来检验中介效应的存在性。模拟研究表明，所提方法在IIE估计精度、对应检验的I类错误率和检验效能方面均具有良好的有限样本表现。在AML微生物组研究中，我们的发现表明诱导化疗强度水平对感染的影响主要由患者的肠道微生物组介导。
英文摘要：
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 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 induction chemotherapy 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
