Published May 21, 2025
| Version v3
Dataset
Open
Repository of posterior distributions from Bayesian benchmark dose analysis
Authors/Creators
Description
This repository contains posterior distributions for all model parameters obtained from analysis of continuous and quantal dose-response studies.
The attached repositories contain the following variables:
| Variable | Description |
| dataID | unique identifier for each dataset |
| Compound | compound investigated |
| CASnr | CAS number of the compound investigated |
| Endpoint | endpoint investigated |
| Species | animal species investigated |
| Strain | animal strain investigated |
| Sex | animal sex investigated |
| Model | 16 models for continuous endpoints, 8 models for quantal endpoints; MA = model-averaged; N = normal distribution; LN = lognormal distribution |
| Parameter | bmd, d, mu_0, mu_inf, sigma^2 |
| Weight | weight for the corresponding model, as used in model averaging |
| bf.model | Bayes factor of the model compared to the saturated model; only given if Weight > 0 |
| AnalysisRun | indicator whether the analysis was run |
| Monotone | indicator whether the data are monotone |
| DR.effect | Bayes factor for dose-response test |
| Suitable | indicator whether the data are suitable for analysis |
| min.samplesize | minimum dose-specific sample size of the dataset |
| max.samplesize | maximum dose-specific sample size of the dataset |
| NDose | number of dose groups in the dataset |
| Q1-Q1000 | quantiles of the posterior distribution |
| source | source from which the dataset was obtained |
| source_ID | unique identifier for each dataset by source |
The repository for continuous endpoints has the following additional variables:
| Variable | Description |
| const.var.p | p-value of Bartlett test for constant variance |
| const.cv.p | p-value of Bartlett test for constant coefficient of variation (i.e. constant variance on log-scale) |
| norm.p | p-value of Shapiro-Wilks test for normality |
| lognorm.p | p-value of Shapiro-Wilks test for lognormality |