Mediation analysis is an important tool to study causal associations in biomedical and other scientific areas and has recently gained attention in microbiome studies. Using a microbiome study of acute myeloid leukemia (AML) patients, we investigate whether the effect of induction chemotherapy intensity levels on the infection status is mediated by the microbial taxa abundance. The unique characteristics of the microbial mediators -- high-dimensionality, zero-inflation, and dependence -- call for new methodological developments in mediation analysis. The presence of an exposure-induced mediator-outcome confounder, antibiotic use, further requires a delicate treatment in the analysis. To address these unique challenges in our motivating AML microbiome study, we propose a novel nonparametric identification formula for the interventional indirect effect (IIE), a measure recently developed for studying mediation effects. We develop the corresponding estimation algorithm using the inverse probability weighting method. We also test the presence of mediation effects via constructing the standard normal bootstrap confidence intervals. Simulation studies show that the proposed method has good finite-sample performance in terms of the IIE estimation, and type-I error rate and power of the corresponding test. In the AML microbiome study, our findings suggest that the effect of induction chemotherapy intensity levels on infection is mainly mediated by patients' gut microbiome.

翻译：中介分析是研究生物医学及其他科学领域因果关联的重要工具，近年来在微生物组研究中受到广泛关注。本研究利用急性髓系白血病（AML）患者的微生物组数据，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所中介。微生物中介因子具有高维性、零膨胀性和依赖性等独特特征，这要求中介分析方法学需进行创新性发展。此外，暴露诱导的中介-结局混杂因素（抗生素使用）的存在，更要求分析过程中进行精细处理。为解决本项AML微生物组研究中的上述独特挑战，我们提出了一种新颖的非参数识别公式，用于估计近期为研究中介效应而开发的干预间接效应（IIE）。我们采用逆概率加权方法开发了相应的估计算法，并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。模拟研究表明，所提方法在IIE估计、检验的第一类错误率及统计功效方面均表现出良好的有限样本性能。在AML微生物组研究中，我们的发现表明诱导化疗强度对感染的影响主要由患者肠道微生物组所中介。