Ferrari Federico, Dunson David B
Department of Statistical Science, Duke University.
J Am Stat Assoc. 2021;116(535):1521-1532. doi: 10.1080/01621459.2020.1745813. Epub 2020 Apr 20.
This article is motivated by the problem of inference on interactions among chemical exposures impacting human health outcomes. Chemicals often co-occur in the environment or in synthetic mixtures and as a result exposure levels can be highly correlated. We propose a latent factor joint model, which includes shared factors in both the predictor and response components while assuming conditional independence. By including a quadratic regression in the latent variables in the response component, we induce flexible dimension reduction in characterizing main effects and interactions. We propose a Bayesian approach to inference under this Factor analysis for INteractions (FIN) framework. Through appropriate modifications of the factor modeling structure, FIN can accommodate higher order interactions. We evaluate the performance using a simulation study and data from the National Health and Nutrition Examination Survey (NHANES). Code is available on GitHub.
本文受化学暴露对人类健康结果影响的相互作用推断问题的启发。化学物质经常在环境中或合成混合物中共存，因此暴露水平可能高度相关。我们提出了一种潜在因素联合模型，该模型在预测变量和响应变量组件中都包含共享因素，同时假设条件独立性。通过在响应变量组件的潜在变量中纳入二次回归，我们在表征主效应和相互作用时引入了灵活的降维方法。我们在这个交互作用因子分析（FIN）框架下提出了一种贝叶斯推断方法。通过对因子建模结构进行适当修改，FIN可以适应高阶相互作用。我们使用模拟研究和来自国家健康与营养检查调查（NHANES）的数据评估了该模型的性能。代码可在GitHub上获取。
