When assessing the human risks due to exposure to environmental chemicals, traditional dose-response analyses are not straightforward when there are numerous high-quality epidemiological studies of priority cancer and non-cancer health outcomes. Given this wealth of information, selecting a single “best” study on which to base dose-response analyses is difficult and would potentially ignore much of the available data. Therefore, systematic approaches are necessary for the analysis of these rich databases. Examples are meta-analysis (and further, meta-regression), which are well established methods that consider and incorporate information from multiple studies into the estimation of risks due to exposure to environmental contaminants. In this paper, we propose a hierarchical, Bayesian meta-analysis approach for the dose-response analysis of multiple epidemiological studies. This paper is the second of two papers detailing this approach; the first covered “pre-analysis” steps necessary to prepare the data for dose-response modeling. This paper focuses on the hierarchical Bayesian approach to dose-response modeling and extrapolation of risk to populations of interest using the association between bladder cancer and oral inorganic arsenic (iAs) exposure as an illustrative case study. In particular, this paper addresses the modeling of both case-control and cohort studies with a flexible, logistic model in a hierarchical Bayesian framework that estimates study-specific slopes, as well as a pooled slope across all studies. This approach is akin to a random effects model in which no assumption is made a priori that there is a single, common slope for all included studies. Further, this paper also details extrapolation of the estimates of logistic slope to extra risk in a target population using a lifetable analysis and basic assumptions about background iAs exposure levels. In this case, the target population was the general United States population and information on all-cause mortality and incidence and mortality from bladder cancer was used to perform the lifetable analysis. The methods herein were developed for general use in investigating the association between any pollutant and observed health-effects in epidemiological studies. In order to demonstrate these methods, inorganic arsenic was chosen as a case study given the large epidemiological database that exists for this contaminant.

在评估因暴露于环境化学物质而造成的人类风险时，传统的剂量反应分析并不简单，因为有大量关于优先癌症和非癌症健康结果的高质量流行病学研究。考虑到如此丰富的信息，选择一项“最佳”研究作为剂量反应分析的基础是很困难的，并且可能会忽略大部分可用数据。因此，需要系统的方法来分析这些丰富的数据库。例如荟萃分析（以及进一步的荟萃回归），这是一种行之有效的方法，考虑并将多项研究的信息纳入对因暴露于环境污染物而造成的风险的估计中。在本文中，我们提出了一种分层贝叶斯荟萃分析方法，用于多项流行病学研究的剂量反应分析。本文是详细介绍此方法的两篇论文中的第二篇；第一个涵盖了为剂量反应建模准备数据所需的“预分析”步骤。本文重点介绍剂量反应模型的分层贝叶斯方法以及使用膀胱癌和口服无机砷 (iAs) 暴露之间的关联作为说明性案例研究来推断感兴趣人群的风险。特别是，本文讨论了病例对照和队列研究的建模，在分层贝叶斯框架中使用灵活的逻辑模型来估计研究特定的斜率以及所有研究的汇总斜率。这种方法类似于随机效应模型，其中没有先验地假设所有纳入的研究都有一个单一的、共同的斜率。此外，本文还详细介绍了使用生命周期分析和关于背景 iAs 暴露水平的基本假设，对目标人群中的额外风险进行逻辑斜率估计的外推。在本例中，目标人群是美国普通人群，并且使用有关全因死亡率以及膀胱癌发病率和死亡率的信息来进行生命表分析。本文开发的方法一般用于调查流行病学研究中任何污染物与观察到的健康影响之间的关联。为了演示这些方法，考虑到该污染物存在大量流行病学数据库，选择无机砷作为案例研究。