An extended and unified modeling framework for benchmark dose estimation for both continuous and binary data. Issue 7 (25th June 2020)

• Record Type: Journal Article
• Authors: Aerts, Marc; Wheeler, Matthew W.; Abrahantes, José Cortiñas
• Is Part Of: Environmetrics. Volume 31:Issue 7(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-06-25
• Subjects: Akaike information criterion -- benchmark dose -- bootstrap -- cumulative distribution function -- dose response -- maximum likelihood -- model averaging; Environmental sciences -- Statistical methods -- Periodicals; 550.72
• DOI: 10.1002/env.2630
• Languages: English
• ISSNs: 1180-4009

Abstract

Abstract: 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.