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：评估多种暴露物对人类健康的联合效应引起了广泛关注，这在环境流行病学和毒理学中常被称为混合物问题。传统上，研究逐个考察不同化学物质的不良健康效应，但人们担忧某些化学物质可能共同作用，放大彼此的效应。这种放大作用称为协同交互作用，而相互抑制效应的化学物质则存在拮抗交互作用。当前评估化学混合物健康效应的方法未在模型中明确考虑协同或拮抗作用，而是侧重于参数化或无约束的非参数化剂量反应曲面建模。参数化方法可能过于僵化，而非参数化方法面临维数灾难，导致曲面估计过度波动且难以解释。我们提出一种贝叶斯方法，将响应曲面分解为可加主效应和两两交互效应，进而检测协同与拮抗交互作用。同时提供每个交互分量的变量选择决策。这一协同拮抗交互作用检测（SAID）框架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，与现有方法进行了比较评估。