Department of Biostatistics, Virginia Commonwealth University, Richmond, Virginia, USA.
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, Maryland, USA.
Stat Med. 2024 Mar 30;43(7):1441-1457. doi: 10.1002/sim.10022. Epub 2024 Feb 2.
Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one-dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi-center case-control study of non-Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.
混合物分析是一种新兴的统计学工具，用于流行病学研究，旨在估计与多种暴露混合相关的健康影响。这种方法承认个体同时经历多种暴露，并且可以估计混合物中各成分的相对重要性。由于混合物而产生的健康影响可能因空间而异，这是由政治、人口、环境或其他差异驱动的。在这种情况下，如果不考虑空间变化来估计全球混合物效应，就会导致效应估计的偏差，并可能降低统计能力。迄今为止，还没有开发出估计空间变化化学混合物效应的方法。我们开发了一种贝叶斯空间变化混合物模型，该模型估计空间变化的混合物效应和混合物中各成分的重要性权重，同时调整协变量。我们通过一项模拟研究来证明该模型的功效，该研究改变了混合物的数量（一个和两个）和空间模式（全局、一维、径向）以及混合物效应的幅度，表明该模型能够准确再现各种场景中混合物效应的空间模式。最后，我们将我们的模型应用于底特律、爱荷华州、洛杉矶和西雅图的非霍奇金淋巴瘤（NHL）多中心病例对照研究。我们发现爱荷华州两种农药混合物与 NHL 存在显著的正相关和负相关的空间变化，而在其他三个中心没有发现强烈的空间效应。总之，贝叶斯空间变化混合物模型代表了一种用于建模混合物效应空间变化的新方法。
