当前位置: X-MOL 学术Environmetrics › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
An Extended and Unified Modeling Framework for Benchmark Dose Estimation for Both Continuous and Binary Data
Environmetrics ( IF 1.7 ) Pub Date : 2020-06-25 , DOI: 10.1002/env.2630
Marc Aerts 1 , Matthew W Wheeler 2 , José Cortiñas Abrahantes 3
Affiliation  

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.

中文翻译:

用于连续数据和二进制数据的基准剂量估计的扩展统一建模框架

防护和安全当局建议使用模型平均来确定基准剂量方法,作为一种科学上更先进的方法,与用于获取参考点和得出基于健康的指导值的未观察到的不良影响水平方法相比。然而,模型平均在很大程度上取决于候选剂量反应模型的集合,并且这样的集合应该足够丰富以确保包含拟合良好的模型。当前应用的连续端点候选模型集通常仅限于两种模型,即指数模型和希尔模型,并且与当前用于二元端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,该框架可应用于连续终点和二元终点,并涵盖这两种类型终点的当前模型。与引导程序相结合,该框架提供了一种统一的基准剂量估计方法。该方法使用两个数据集进行说明,一个数据集具有连续端点,另一个数据集具有二进制端点。
更新日期:2020-06-25
down
wechat
bug