An important goal of environmental epidemiology is to quantify the complex health risks posed by a wide array of environmental exposures. In analyses focusing on a smaller number of exposures within a mixture, flexible models like Bayesian kernel machine regression (BKMR) are appealing because they allow for non-linear and non-additive associations among mixture components. However, this flexibility comes at the cost of low power and difficult interpretation, particularly in exposomic analyses when the number of exposures is large. We propose a flexible framework that allows for separate selection of additive and non-additive effects, unifying additive models and kernel machine regression. The proposed approach yields increased power and simpler interpretation when there is little evidence of interaction. Further, it allows users to specify separate priors for additive and non-additive effects, and allows for tests of non-additive interaction. We extend the approach to the class of multiple index models, in which the special case of kernel machine-distributed lag models are nested. We apply the method to motivating data from a subcohort of the Human Early Life Exposome (HELIX) study containing 65 mixture components grouped into 13 distinct exposure classes.

翻译：环境流行病学的一个重要目标是量化由大量环境暴露所构成的复杂健康风险。在关注混合物中较少数量暴露的分析中，贝叶斯核机器回归（BKMR）等灵活模型具有吸引力，因为它们允许混合物组分之间存在非线性和非加性关联。然而，这种灵活性是以低统计功效和难以解释为代价的，特别是在暴露数量庞大的暴露组学分析中。我们提出了一个灵活的框架，允许对加性和非加性效应进行独立选择，从而统一了加性模型与核机器回归。当交互作用的证据很少时，所提出的方法能提高统计功效并简化解释。此外，它允许用户为加性和非加性效应指定独立的先验分布，并允许进行非加性交互作用的检验。我们将该方法推广到多指标模型类，其中核机器分布滞后模型作为特例被嵌套其中。我们将该方法应用于来自人类早期生命暴露组（HELIX）研究的一个子队列的激励数据，该数据包含65个混合物组分，分为13个不同的暴露类别。