Kundu Debamita, Kim Sungduk, Ward Mary H, Albert Paul S
Biostatistics Division, Public Health Sciences, School of Medicine, University of Virginia, Charlottesville, VA, USA.
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA.
Stat Biosci. 2024 Jul;16(2):503-519. doi: 10.1007/s12561-023-09415-4. Epub 2024 Feb 23.
Properly assessing the effects of environmental chemical exposures on disease risk remains a challenging problem in environmental epidemiology. Various analytic approaches have been proposed, but there are few papers that have compared the performance of different statistical methods on a single dataset. In this paper, we compare different regression-based approaches for estimating interactions between chemical mixture components using data from a case-control study on non-Hodgkin's lymphoma. An analytic challenge is the high percentage of exposures that are below the limit of detection (LOD). Using imputation for LOD, we compare different Bayesian shrinkage prior approaches including an approach that incorporates the hierarchical principle where interactions are only included when main effects exist. Further, we develop an approach where main and interactive effects are represented by a series of distinct latent functions. We also fit the Bayesian kernel machine regression to these data. All of these approaches show little evidence of an interaction among the chemical mixtures when measurements below the LOD were imputed. The imputation approach makes very strong assumptions about the relationship between exposure and disease risk for measurements below the LOD. As an alternative, we show the results of an analysis where we model the exposure relationship with two parameters per mixture component; one characterizing the effect of being below the LOD and the other being a linear effect above the LOD. In this later analysis, we identify numerous strong interactions that were not identified in the analyses with imputation. This case study demonstrated the importance of developing new approaches for mixtures when the proportions of exposure measurements below the LOD are high.
正确评估环境化学物质暴露对疾病风险的影响仍然是环境流行病学中一个具有挑战性的问题。人们已经提出了各种分析方法，但很少有论文在单个数据集上比较不同统计方法的性能。在本文中，我们使用一项关于非霍奇金淋巴瘤的病例对照研究数据，比较了基于回归的不同方法来估计化学混合物成分之间的相互作用。一个分析挑战是低于检测限（LOD）的暴露比例很高。使用LOD的插补法，我们比较了不同的贝叶斯收缩先验方法，包括一种纳入分层原则的方法，即只有在存在主效应时才纳入相互作用。此外，我们开发了一种方法，其中主效应和交互效应由一系列不同的潜在函数表示。我们还将贝叶斯核机器回归应用于这些数据。当对低于LOD的测量值进行插补时，所有这些方法都几乎没有显示出化学混合物之间存在相互作用的证据。插补方法对低于LOD的测量值的暴露与疾病风险之间的关系做出了非常强的假设。作为一种替代方法，我们展示了一项分析的结果，在该分析中，我们对每个混合物成分用两个参数对暴露关系进行建模；一个表征低于LOD的效应，另一个表征高于LOD的线性效应。在后面的这项分析中，我们识别出了许多在插补分析中未识别出的强相互作用。这个案例研究表明，当低于LOD的暴露测量比例很高时，开发针对混合物的新方法非常重要。
