Department of Biostatistics, School of Medicine, Virginia Commonwealth University, One Capitol Square, 830 East Main Street, Richmond, VA 23298, USA.
Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20850, USA.
Int J Environ Res Public Health. 2022 Jan 26;19(3):1369. doi: 10.3390/ijerph19031369.
There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian group index regression by treating both regression effects and missing BDL observations as parameters in a model estimated through a Markov chain Monte Carlo algorithm that we refer to as pseudo-Gibbs imputation. We compare this with other Bayesian imputation methods found in the literature (Multiple Imputation by Chained Equations and Sequential Full Bayes imputation) as well as with a non-Bayesian single-imputation method. To evaluate our proposed method, we conduct simulation studies with varying percentages of BDL missingness and strengths of association. We apply our method to the California Childhood Leukemia Study (CCLS) to estimate concentrations of chemicals in house dust in a mixture analysis of potential environmental risk factors for childhood leukemia. Our results indicate that pseudo-Gibbs imputation has superior power for exposure effects and sensitivity for identifying individual chemicals at high percentages of BDL missing data. In the CCLS, we found a significant positive association between concentrations of polycyclic aromatic hydrocarbons (PAHs) in homes and childhood leukemia as well as significant positive associations for polychlorinated biphenyls (PCBs) and herbicides among children from the highest quartile of household income. In conclusion, pseudo-Gibbs imputation addresses a commonly encountered problem in environmental epidemiology, providing practitioners the ability to jointly estimate the effects of multiple chemical exposures with high levels of BDL missingness.
人们越来越感兴趣的是通过混合分析来确定与人类疾病相关的众多化学暴露。在本文中，我们通过贝叶斯组索引回归来解决混合分析中低于检测限（BDL）缺失数据的问题，将回归效应和缺失的 BDL 观测值都视为模型中的参数，通过马尔可夫链蒙特卡罗算法进行估计，我们称之为伪 Gibbs 插补。我们将其与文献中发现的其他贝叶斯插补方法（链式方程多重插补和序贯全贝叶斯插补）以及非贝叶斯单插补方法进行了比较。为了评估我们提出的方法，我们进行了模拟研究，其中包括不同百分比的 BDL 缺失和关联强度。我们将我们的方法应用于加利福尼亚儿童白血病研究（CCLS），以在潜在环境危险因素的混合分析中估计房屋灰尘中化学物质的浓度。我们的结果表明，伪 Gibbs 插补在暴露效应方面具有更高的功效，并且在高 BDL 缺失数据百分比下识别单个化学物质的灵敏度更高。在 CCLS 中，我们发现家中多环芳烃（PAHs）的浓度与儿童白血病之间存在显著正相关，以及来自家庭收入最高四分位数的儿童中多氯联苯（PCBs）和除草剂之间存在显著正相关。总之，伪 Gibbs 插补解决了环境流行病学中常见的问题，为从业者提供了联合估计具有高水平 BDL 缺失的多种化学暴露效应的能力。
