Applications of "Dose Response" Outside of Biostatistics? I was reading about a class of models called "Dose Response Models" (https://cran.r-project.org/web/packages/drda/vignettes/drda.pdf) - in the traditional sense, these are typically used to model the statistical impact and magnitude of some treatment administered on groups of patients.
By doing a quick search on the internet, (naturally) most of the results involving applications of "Dose Response Models" generally concern themselves with estimating the impact of different drug therapies in clinical trials. However, I had the following question:

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*Mathematically, is there anything preventing "Dose Response Models" to be used outside the  their typical use cases in the domain of biostatistics and clinical trials? For instance, could a bank use "Dose Response Methodology" to estimate the long-term impact of setting different interest rates on bankruptcy of their borrowers - or is there something inherently preventing these models from being used outside of their typical use cases in clinical trials?

I am not sure that the typical application of dose-response methodology is in clinical trials, I thought it was toxicology. I was once helping a chemistry student, Ximena, her (principal) advisor where interested in searching for new molecules with a potential of medical use (among little-investigated amazonian plants). Extracts where distillated multiple times, extracts at different heights of the distillation tower where submitted to bioessay using the organism Artemia Salina, the Lethal Dose 50 estimated, and then the extract with lowest lethal dose were chosen to continue the process. At the end it was found a pure extract, which was identified and its molecular structure analyzed (by sending it to a lab in France).
So that is one example of use. As for uses longer away from biology and chemistry, try google scholar (I don't like to advertize google, but competing search engines are not yet up to such tasks ...). I found
Towards a new paradigm in fire severity research using dose–response experiments. The abstract reads:

Most landscape-scale fire severity research relies on correlations between field measures of fire effects and relatively simple spectral reflectance indices that are not direct measures of heat output or changes in plant physiology. Although many authors have highlighted limitations of this approach and called for improved assessments of severity, others have suggested that the operational utility of such a simple approach makes it acceptable. An alternative pathway to evaluate fire severity that bridges fire combustion dynamics and ecophysiology via dose–response experiments is presented. We provide an illustrative example from a controlled nursery combustion laboratory experiment. In this example, severity is defined through changes in the ability of the plant to assimilate carbon at the leaf level. We also explore changes in the Differenced Normalised Differenced Vegetation Index (dNDVI) and the Differenced Normalised Burn Ratio (dNBR) as intermediate spectral indices. We demonstrate the potential of this methodology and propose dose–response metrics for quantifying severity in terms of carbon cycle processes.

