Aerts Marc, Wheeler Matthew W, Abrahantes José Cortiñas
Data Science Institute, Interuniversity Institute for Biostatistics and statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium.
National Institute for Occupational Safety and Health, Cincinnati, Ohio.
Environmetrics. 2020 Nov;31(7). doi: 10.1002/env.2630. Epub 2020 May 16.
Protection and safety authorities recommend the use of model averaging to determine the benchmark dose approach as a scientifically more advanced method compared with the no-observed-adverse-effect-level approach for obtaining a reference point and deriving health-based guidance values. Model averaging however highly depends on the set of candidate dose-response models and such a set should be rich enough to ensure that a well-fitting model is included. The currently applied set of candidate models for continuous endpoints is typically limited to two models, the exponential and Hill model, and differs completely from the richer set of candidate models currently used for binary endpoints. The objective of this article is to propose a general and wide framework of dose response models, which can be applied both to continuous and binary endpoints and covers the current models for both type of endpoints. In combination with the bootstrap, this framework offers a unified approach to benchmark dose estimation. The methodology is illustrated using two data sets, one with a continuous and another with a binary endpoint.
保护与安全当局建议采用模型平均法来确定基准剂量方法，因为与无观察到不良反应水平方法相比，该方法在科学上更为先进，可用于获取参考点并推导基于健康的指导值。然而，模型平均法高度依赖于候选剂量反应模型集，且该模型集应足够丰富，以确保包含一个拟合良好的模型。目前用于连续终点的候选模型集通常限于两种模型，即指数模型和希尔模型，这与目前用于二元终点的更丰富的候选模型集完全不同。本文的目的是提出一个通用且广泛的剂量反应模型框架，该框架可同时应用于连续终点和二元终点，并涵盖这两种终点类型的当前模型。结合自助法，该框架提供了一种统一的基准剂量估计方法。使用两个数据集对该方法进行了说明，一个数据集具有连续终点，另一个具有二元终点。
