Statistical methods for identifying harmful chemicals in a correlated mixture often assume linearity in exposure‐response relationships. Nonmonotonic relationships are increasingly recognized (eg, for endocrine‐disrupting chemicals); however, the impact of nonmonotonicity on exposure selection has not been evaluated. In a simulation study, we assessed the performance of Bayesian kernel machine regression (BKMR), Bayesian additive regression trees (BART), Bayesian structured additive regression with spike‐slab priors (BSTARSS), generalized additive models with double penalty (GAMDP) and thin plate shrinkage smoothers (GAMTS), multivariate adaptive regression splines (MARS), and lasso penalized regression. We simulated realistic exposure data based on pregnancy exposure to 17 phthalates and phenols in the US National Health and Nutrition Examination Survey using a multivariate copula. We simulated data sets of size N = 250 and compared methods across 32 scenarios, varying by model size and sparsity, signal‐to‐noise ratio, correlation structure, and exposure‐response relationship shapes. We compared methods in terms of their sensitivity, specificity, and estimation accuracy. In most scenarios, BKMR, BSTARSS, GAMDP, and GAMTS achieved moderate to high sensitivity (0.52‐0.98) and specificity (0.21‐0.99). BART and MARS achieved high specificity (≥0.90), but low sensitivity in low signal‐to‐noise ratio scenarios (0.20‐0.51). Lasso was highly sensitive (0.71‐0.99), except for quadratic relationships (≤0.27). Penalized regression methods that assume linearity, such as lasso, may not be suitable for studies of environmental chemicals hypothesized to have nonmonotonic relationships with outcomes. Instead, BKMR, BSTARSS, GAMDP, and GAMTS are attractive methods for flexibly estimating the shapes of exposure‐response relationships and selecting among correlated exposures.

中文翻译：

---

用于估计相关化学混合物的非线性健康影响的变量和函数选择方法的性能：模拟研究。

用于识别相关混合物中有害化学物质的统计方法通常假定暴露-反应关系呈线性。非单调关系得到越来越多的认可（例如，用于破坏内分泌的化学物质）；但是，尚未评估非单调性对暴露选择的影响。在仿真研究中，我们评估了贝叶斯核仁机器回归（BKMR），贝叶斯加性回归树（BART），带穗状先验先验的贝叶斯结构化加性回归（BSTARSS），具有双罚分的广义加性模型（GAMDP）和稀薄性能板收缩平滑器（GAMTS），多元自适应回归样条（MARS）和套索惩罚回归。我们在美国国家健康与营养调查中使用多变量copula，基于怀孕时暴露于17种邻苯二甲酸盐和苯酚的情况，模拟了实际的暴露数据。我们模拟了大小的数据集ñ = 250，并比较了32种情况下的方法，这些方法因模型大小和稀疏性，信噪比，相关结构和暴露-响应关系形状而异。我们比较了方法的敏感性，特异性和估计准确性。在大多数情况下，BKMR，BSTARSS，GAMDP和GAMTS达到中度到高灵敏度（0.52-0.98）和特异性（0.21-0.99）。BART和MARS具有高特异性（≥0.90），但在低信噪比情况下（0.20-0.51）灵敏度较低。套索是高度敏感的（0.71-0.99），除了二次关系（≤0.27）。假设线性的惩罚回归方法（例如套索）可能不适用于假设与结果具有非单调关系的环境化学物质的研究。而是BKMR，BSTARSS，GAMDP，