Kim Sungduk, Chen Zhen, Perkins Neil J, Schisterman Enrique F, Buck Louis Germaine M
Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD 20892, USA.
Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, MD 20892, USA.
Stat Biosci. 2019 Dec;11(3):524-547. doi: 10.1007/s12561-019-09243-5. Epub 2019 Jun 7.
Human exposure to persistent environmental pollutants often results in concentrations with a range of values below the laboratory detection limits. Growing evidence suggests that inadequate handling of concentrations below the limit of detection (LOD) may bias assessment of health effects in relation to environmental exposures. We seek to quantify such bias in models focusing on the day-specific probability of pregnancy during the fertile window and propose a model-based approach to reduce such bias. A multivariate skewed generalized -distribution constrained by the LOD is assumed for the chemical concentrations, which realistically represents the underlying distribution. A latent variable-based framework is used to model fecundibility, which nonlinearly relates conception probability to chemical concentrations, daily intercourses, and other important covariates. The advantages of the proposed approach include the use of multiple chemical concentrations to aid the estimation of left censored chemical exposures, as well as the model-based feedback mechanism for fecundibility outcome to inform the estimations, and an adequate handling of model uncertainty through a joint modeling framework. A Markov chain Monte Carlo sampling algorithm is developed for implementing the Bayesian computations and the logarithm of pseudo-marginal likelihood measure is used for model choices. We conduct simulation studies to demonstrate the performance of the proposed approach and apply the framework to the Longitudinal Investigation of Fertility and the Environment study which evaluates the effects of exposures to environmental pollutants on the probability of pregnancy. We found that '-DDT is negatively associated with the day-specific probability of pregnancy.
人类接触持久性环境污染物往往会导致浓度处于一系列低于实验室检测限的值范围内。越来越多的证据表明，对低于检测限（LOD）的浓度处理不当可能会使与环境暴露相关的健康影响评估产生偏差。我们试图在关注育龄期特定日期怀孕概率的模型中量化这种偏差，并提出一种基于模型的方法来减少这种偏差。对于化学浓度，假设采用受LOD约束的多元偏态广义分布，这实际代表了潜在分布。基于潜在变量的框架用于对受孕能力进行建模，该框架将受孕概率与化学浓度、每日性交次数及其他重要协变量进行非线性关联。所提方法的优点包括使用多种化学浓度来辅助估计左删失的化学暴露，以及基于模型的受孕能力结果反馈机制以指导估计，还通过联合建模框架对模型不确定性进行了充分处理。开发了一种马尔可夫链蒙特卡罗抽样算法来进行贝叶斯计算，并使用伪边际似然度量的对数进行模型选择。我们进行了模拟研究以证明所提方法的性能，并将该框架应用于生育与环境纵向调查研究，该研究评估环境污染物暴露对怀孕概率的影响。我们发现，γ-滴滴涕与特定日期的怀孕概率呈负相关。
