ICF, 9300 Lee Highway, Fairfax, VA 22031-1207, USA.
Department of Environmental and Occupational Health, Indiana University, Bloomington, IN, USA.
Environ Int. 2020 Oct;143:105857. doi: 10.1016/j.envint.2020.105857. Epub 2020 Jun 29.
This paper describes the use of multiple models and model averaging for considering dose-response uncertainties when extrapolating low-dose risk from studies of populations with high levels of exposure. The model averaging approach we applied builds upon innovative methods developed by the U.S. Food and Drug Administration (FDA), principally through the relaxing of model constraints. The relaxing of model constraints allowed us to evaluate model uncertainty using a broader set of model forms and, within the context of model averaging, did not result in the extreme supralinearity that is the primary concern associated with the application of individual unconstrained models. A study of the relationship between inorganic arsenic exposure to a Taiwanese population and potential carcinogenic effects is used to illustrate the approach. We adjusted the reported number of cases from two published prospective cohort studies of bladder and lung cancer in a Taiwanese population to account for potential covariates and less-than-lifetime exposure (for estimating effects on lifetime cancer incidence), used bootstrap methods to estimate the uncertainty surrounding the µg/kg-day inorganic arsenic dose from drinking water and dietary intakes, and fit multiple models weighted by Bayesian Information Criterion to the adjusted incidence and dose data to generate dose-specific mean, 2.5th and 97.5th percentile risk estimates. Widely divergent results from adequate model fits for a broad set of constrained and unconstrained models applied individually and in a model averaging framework suggest that substantial model uncertainty exists in risk extrapolation from estimated doses in the Taiwanese studies to lower doses more relevant to countries like the U.S. that have proportionally lower arsenic intake levels.
本文描述了在从高暴露人群研究中推断低剂量风险时，使用多种模型和模型平均来考虑剂量-反应不确定性的方法。我们应用的模型平均方法建立在美国食品和药物管理局 (FDA) 开发的创新方法的基础上，主要通过放宽模型约束。放宽模型约束使我们能够使用更广泛的模型形式评估模型不确定性，并在模型平均的背景下，不会导致与应用单个无约束模型相关的主要问题——极端超线性。我们使用台湾人群无机砷暴露与潜在致癌作用的关系研究来说明这种方法。我们调整了两份已发表的台湾人群膀胱癌和肺癌前瞻性队列研究报告的病例数，以考虑潜在的协变量和非终生暴露（用于估计对终生癌症发病率的影响），使用自举方法估计饮用水和膳食摄入中无机砷µg/kg-天剂量的不确定性，并拟合多个模型，这些模型的权重由贝叶斯信息准则决定，以调整后的发病率和剂量数据生成剂量特异性平均、2.5 百分位和 97.5 百分位风险估计。广泛的约束和非约束模型的充分模型拟合结果以及模型平均框架中的结果差异表明，从台湾研究中估计的剂量推断到更符合美国等砷摄入量相对较低的国家的较低剂量时，存在大量的模型不确定性。
