No-Observed-Adverse-Effect Levels in Severity Data
本文提出一种基于AIC的统计方法，用于从多级严重程度毒性数据中估计未观察到有害作用水平（NOAEL），并通过啮齿动物实验数据验证了该方法。
Abstract Toxicity data are often categorized by severity of response and dose level with the assumption that there is a tolerated dose below which there is no toxicity. For data from a controlled experiment, the largest observed dose at or below the tolerated dose is called the no-observed-adverse-effect level (NOAEL). The problem of identifying the NOAEL can be viewed statistically as estimating the maximal observed dose for which there is no increased severity or frequency of toxic response. We previously proposed a method based on the Akaike information criterion (AIC) for the case with only two response levels (presence or absence of a toxic endpoint). We show here that repeated applications of that method to suitably defined subsets of data provide the maximum penalized likelihood estimate of the NOAEL when there are multiple severity levels, under a slight modification of the continuation-ratio logit model. Three sets of data on controlled exposure of rodents are used to illustrate the method.