Benchmark Dose (BMD) Modeling in Human Health Risk Assessments training slides
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In 2024, EPA released a new version of BMDS Online (https://bmdsonline.epa.gov/), a browser-based version of BMDS to allow users to run BMDS on any computer with access to the internet, that includes a multitumor cancer model and a nested dichotomous model for developmental toxicity data. EPA also released BMDS Desktop (a Python-based graphical user interface) to replace the Excel-based BMDS 3.3, as well as pybmds, which allows users to run dose-response analyses in a scripting environment and run high-throughput or batch analyses of 1000s of individual datasets. Additionally, NIEHS has further expanded dose-response capabilities through the release of the R-based ToxicR Bayesian modeling platform that “untethers” BMDS and other models from standard parameterizations, expanding its capabilities for research applications. This workshop will cover dose-response analyses commonly performed in human health risk assessments and participants will learn how to conduct dose-response modeling of dichotomous, continuous, cancer, and developmental toxicity response data using BMDS Online, BMDS Desktop, and pybmds. Demos of pybmds will additionally highlight features of the package that allow for scripted batch processing, advanced graphics, and custom BMD analyses. Following these introductory analyses, participants will learn and practice the use of Bayesian models, including the application of a Bayesian framework for model averaging using ToxicR. Participants will explore model averaging approaches for dichotomous and continuous data, including new model averaging capabilities for continuous data that include the European Food Safety Authority’s (EFSA) suite of continuous models currently only available in ToxicR. The research functionality and modeling capacity of the ToxicR platform will be demonstrated. Hands-on exercises in ToxicR will be provided. Participants will be shown how to modify prior assumptions and perform sensitivity analyses to investigate the default prior’s effect on a given analysis. Additional features of the package that will also be highlighted.