Bayesian Model Averaging for Benchmark Dose Analysis in Developmental Toxicology

Khorsheed, Eman; Razzaghi, Mehdi

doi:10.18576/amis/130101

Cited by 12 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It was shown that the derived estimates more accurately reflect uncertainty in the understanding of the effects of exposure on the occurrence of adverse responses. (Khorsheed and Razzaghi, 2019)…”

Section: Contents I Introductionmentioning

confidence: 99%

EFSA Platform for Bayesian Benchmark Dose Analysis

2022

EFSA Supporting Publications

View full text Add to dashboard Cite

The main goal of this grant is to contribute to the harmonization of BMD approaches across international organizations and to contribute to the development of an EFSA online platform that performs Bayesian benchmark dose (BMD) analysis. The objectives include (i) to further develop the prototype Bayesian Model Averaging for continuous data (exploring the inclusion of covariates, as well as exploring models including litter effects) using the new family of models, (ii) to develop Bayesian Model Averaging for quantal data (exploring the inclusion of covariates, as well as exploring models including litter effects) using the new family of models, and (iii) updating the R4EU web platform to include Bayesian model fitting. In this scientific report, we extensively describe the developed methodology. Bayesian Model Averaging was developed for continuous and quantal data using 8 candidate models. Additionally for continuous data, the normal as well as the lognormal distribution are considered, resulting in 16 candidate models. The use of informative prior distributions was implemented for both types of data. For both types of data, the methodology was extended to the case of clustered data, and a first exploration of the inclusion of covariate effects was done.

show abstract

Section: Contents I Introductionmentioning

confidence: 99%