Proast
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Proast is a software for dose-response modeling and benchmark dose analysis. PROAST has been developed at RIVM for the analysis of toxicological dose-response data. It can be used for dose-response modeling of continuous, quantal or ordinal response data, and for deriving a Benchmark dose in human risk assessment, or an ECx in ecotoxicological risk assessment. Currently, PROAST is based on multiple choice questions that guide the user through the program. Next year a version will be released that is based on a graphical user interface (GUI), which should make the use of PROAST even simpler. PROAST is suitable for an in depth analysis of a single dataset, but also for a quick (automated) analysis of a whole series of endpoints (responses), which may be useful for analyzing complete studies. Apart from applications in human or ecotoxicological risk assessment Proast can be used for nonlinear regression in any other field of science. [1]
Scope
What is a good method to estimate and model dose-responses using R software?
Result
Proast software is a good method for this purpose.
An example how a Proast code could look like in Opasnet.
Parameters are given as a table. The parameter values are stored in a database.
| Obs | Parameter | Result |
|---|---|---|
| 1 | Function | 1 |
| 2 | Vmax | 40 |
| 3 | Kd | 3 |
Code is written and run on a wiki page. In this code, the first two rows define how to get the parameter values from the database.
Rationale
Some further specifics are:
- Different subpopulations (e.g. males and females, rats and mice, subchronic and chronic exposures) can be analyzed as a combined dataset. Thus, it can be tested if the subpopulations differ significantly in sensitivity (strength of response). This approach at the same time results in an efficient use of the available data.
- Nested data (e.g. litter effects in developmental studies; housing effects when animals are grouped in different housings) can be dealt with.
- PROAST includes two families of five nested models (exponential and Hill, respectively) from which the optimal model for that family can be determined automatically.
- The analysis of the dose-response data can be directly followed-up by applying probabilistic Assessment Factors (AF), to arrive at a Probabilistic RfD. In addition, by applying probabilistic AFs one may derive a distribution for the predicted effect size in the sensitive human population.
PROAST vs. BMDS
USEPA developed the BMDS software, which is also suitable for dose-response analysis and deriving a BMDL from dose-response data. Currently, efforts are undertaken to achieve consistency between the BMDS and PROAST software.
Compared to the BMDS software, PROAST has some advantages and disadvantages. Advantages are that PROAST has more options and flexibility compared to BMDS, such as including covariates in the analysis, and changing the plotting options. The main disadvantage compared to BMDS is that it is less easy to get PROAST working for the first time. Some initial effort (up to one hour, depending on personal skills) is needed to implement PROAST before it can be used. Further, it may be helpful if users learn some of the basic features of R.
Functions used in Proast
f f.assign f.attach f.BMD.sd.con f.boot.con f.cat f.ced.con f.cedlines.con f.change.settings f.choose.model f.con f.converged f.detach f.disclaim f.execute f.expect.con f.graph.window f.graphwin.size f.grubb f.ini f.lines.con f.mm4.con f.mm5.con f.nested.con f.pars f.plot.all f.plot.con f.plot.sep f.plot.type.lst f.plotlimits f.press.key.to.continue f.proast f.profile.all f.quick.con f.start.con
See also
- Proast software for dose-response modelling (developed by Wout Slob in RIVM). A related article.
Keywords
Dose-response, exposure response function, ERF, regression analysis, Hill plot
References
Related files
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