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Introduction
The benchmark dose (BMD) method has been widely accepted as the preferred method to replace the traditional no (or lowest) observed adverse effect level (NOAEL/LOAEL) approach for dose–response assessment in human health risk assessment. The BMD method has many important advantages over the NOAEL/LOAEL approach, but it requires more sophisticated regression algorithms to fit various dose–response models to the input data. Hence, it is necessary to have well-developed software to facilitate implementation of the BMD method.
There are two major software programs for BMD analysis that have been widely distributed and used by risk assessors and scientists throughout the world. The first is the Benchmark Dose Software [version 2.6.0.1; U.S. Environmental Protection Agency (EPA)] that was originally published by the U.S. EPA in 2000 and has been continuously upgraded and improved. This software is Windows based and has a well-designed graphical user interface (GUI) that is capable of analyzing multiple types of dose–response data, including the two most frequently used types: dichotomous data and continuous data. Over the years, a number of special dose–response models have been added to the software package (e.g., models to handle nested data) for certain specific uses, and some third-party packages (e.g., BMDS Wizard; ICF International) have been built to meet particular needs. The second software program, PROAST, is published by the Netherlands National Institute for Public Health and the Environment (RIVM). PROAST is programmed in the R programming language (R Core Team) and can be used on any operating system where R can be installed (e.g., Windows, Linux, Mac). PROAST is able to analyze dichotomous, continuous, and ordinal dose–response data, and a GUI was recently developed for the latest version, which was published in early 2014 (v.38.9). Both software packages have their respective advantages and are slightly different in some technical details, such as the dose–response models included and default assumptions on the distribution of continuous data. In general, both packages are suitable for dose–response analysis and deriving BMD and its statistical lower bound (BMDL). However, it is important to note that both software approaches utilize a frequentist-based statistical approach (i.e., the maximum likelihood estimation) for dose–response model fitting and parameter estimation.
In this paper, we present a web-based dose–response modeling system featuring an implementation of Bayesian inference...