There is abundant interest in assessing the joint effects of multiple exposures on human health. This is often referred to as the mixtures problem in environmental epidemiology and toxicology. Classically, studies have examined the adverse health effects of different chemicals one at a time, but there is concern that certain chemicals may act together to amplify each other's effects. Such amplification is referred to as synergistic interaction, while chemicals that inhibit each other's effects have antagonistic interactions. Current approaches for assessing the health effects of chemical mixtures do not explicitly consider synergy or antagonism in the modeling, instead focusing on either parametric or unconstrained nonparametric dose response surface modeling. The parametric case can be too inflexible, while nonparametric methods face a curse of dimensionality that leads to overly wiggly and uninterpretable surface estimates. We propose a Bayesian approach that decomposes the response surface into additive main effects and pairwise interaction effects, and then detects synergistic and antagonistic interactions. Variable selection decisions for each interaction component are also provided. This Synergistic Antagonistic Interaction Detection (SAID) framework is evaluated relative to existing approaches using simulation experiments and an application to data from NHANES.

翻译：评估多种暴露对人体健康的联合效应具有广泛的研究意义，这一问题在环境流行病学和毒理学中常被称为混合物问题。传统研究通常逐一考察不同化学物质对健康的不良影响，但人们担忧某些化学物质可能共同作用以增强彼此效应，这种增强被称为协同相互作用；而相互抑制效应的化学物质则存在拮抗相互作用。当前评估化学混合物健康效应的方法未在建模中明确考虑协同或拮抗作用，主要侧重于参数化或无约束非参数化剂量反应曲面建模。参数化方法可能过于僵化，而非参数方法则面临维度灾难，导致曲面估计过度波动且难以解释。本文提出一种贝叶斯方法，将响应曲面分解为加性主效应与成对交互效应，进而检测协同与拮抗相互作用。该方法同时提供各交互成分的变量选择决策。通过模拟实验及对美国国家健康与营养调查（NHANES）数据的应用分析，本研究所提出的协同-拮抗相互作用检测（SAID）框架相较于现有方法展现出优越性能。