National Institute for Occupational Safety and Health, MS C15, 1050 N. Tusculum Avenue, Cincinnati, OH, USA.
Department of Statistics, Miami University, Oxford, OH, USA.
Risk Anal. 2017 Nov;37(11):2107-2118. doi: 10.1111/risa.12762. Epub 2017 May 29.
Quantitative risk assessment often begins with an estimate of the exposure or dose associated with a particular risk level from which exposure levels posing low risk to populations can be extrapolated. For continuous exposures, this value, the benchmark dose, is often defined by a specified increase (or decrease) from the median or mean response at no exposure. This method of calculating the benchmark dose does not take into account the response distribution and, consequently, cannot be interpreted based upon probability statements of the target population. We investigate quantile regression as an alternative to the use of the median or mean regression. By defining the dose-response quantile relationship and an impairment threshold, we specify a benchmark dose as the dose associated with a specified probability that the population will have a response equal to or more extreme than the specified impairment threshold. In addition, in an effort to minimize model uncertainty, we use Bayesian monotonic semiparametric regression to define the exposure-response quantile relationship, which gives the model flexibility to estimate the quantal dose-response function. We describe this methodology and apply it to both epidemiology and toxicology data.
定量风险评估通常从与特定风险水平相关的暴露或剂量估计开始，从中可以推断出对人群低风险的暴露水平。对于连续暴露，该值（基准剂量）通常由特定的中位数或无暴露时的平均值响应增加（或减少）来定义。这种计算基准剂量的方法没有考虑到响应分布，因此不能根据目标人群的概率陈述进行解释。我们研究了分位数回归作为中位数或均值回归的替代方法。通过定义剂量-反应分位数关系和损伤阈值，我们将基准剂量定义为与特定概率相关的剂量，该概率表示人群的反应等于或超过指定的损伤阈值的概率。此外，为了尽量减少模型不确定性，我们使用贝叶斯单调半参数回归来定义暴露-反应分位数关系，这使模型具有灵活性，可以估计定量剂量-反应函数。我们描述了这种方法，并将其应用于流行病学和毒理学数据。
