Gaskins Audrey J, Schisterman Enrique F
Division of Epidemiology, Statistics and Prevention Research, Eunice Kennedy Schriver National Institute of Child Health and Human Development, NIH, Rockville, MD, USA.
Methods Mol Biol. 2009;580:371-81. doi: 10.1007/978-1-60761-325-1_20.
Past literature on exposure to lipophilic agents such as organochlorines (OCs) is conflicting, posing challenges for the interpretation of their potential human health risks. Since blood is often used as a proxy for adipose tissue, it is necessary to model serum lipids when assessing health risks of OCs. Using a simulation study, we evaluated four statistical models (unadjusted, standardized, adjusted, and two-stage) for the analysis of polychlorinated biphenyls (PCBs) exposure, serum lipids, and health outcome risk. Eight candidate true causal scenarios, depicted by directed acyclic graphs, were used to illustrate the ramifications of misspecification of underlying assumptions when interpreting results. Biased results were produced when statistical models that deviated from the underlying causal assumptions were used with the lipid standardization method found to be particularly prone to bias. We concluded that investigators must consider biology, biological medium, laboratory measurement, and other underlying modeling assumptions when devising a statistical model for assessing health outcomes in relation to environmental exposures.
过去关于接触亲脂性物质（如有机氯）的文献存在矛盾之处，这给解释其对人类健康的潜在风险带来了挑战。由于血液常被用作脂肪组织的替代指标，因此在评估有机氯的健康风险时，有必要对血清脂质进行建模。通过一项模拟研究，我们评估了四种统计模型（未调整、标准化、调整和两阶段），用于分析多氯联苯（PCBs）暴露、血清脂质和健康结果风险。用有向无环图描绘的八个候选真实因果情景，用于说明在解释结果时潜在假设错误指定的后果。当使用偏离潜在因果假设的统计模型时，会产生有偏差的结果，其中脂质标准化方法特别容易产生偏差。我们得出结论，研究人员在设计用于评估与环境暴露相关的健康结果的统计模型时，必须考虑生物学、生物介质、实验室测量和其他潜在的建模假设。
