A general theory of effect size, and its consequences for defining the benchmark response (BMR) for continuous endpoints.
A general theory on effect size for continuous data predicts a relationship between maximum response and within-group variation of biological parameters, which is empirically confirmed by results from dose-response analyses of 27 different biological parameters. The theory shows how effect sizes observed in distinct biological parameters can be compared and provides a basis for a generic definition of small, intermediate and large effects. While the theory is useful for experimental science in general, it has specific consequences for risk assessment: it solves the current debate on the appropriate metric for the Benchmark response in continuous data. The theory shows that scaling the BMR expressed as a percent change in means to the maximum response (in the way specified) automatically takes "natural variability" into account. Thus, the theory supports the underlying rationale of the BMR 1 SD. For various reasons, it is, however, recommended to use a BMR in terms of a percent change that is scaled to maximum response and/or within group variation (averaged over studies), as a single harmonized approach.
a National Institute of Public Health and the Environment (RIVM) , Bilthoven , the Netherlands.
Wout
W
Slob
D019985:Benchmarking; D004305:Dose-Response Relationship, Drug; D006801:Humans; D015233:Models, Statistical; D019055:No-Observed-Adverse-Effect Level; D018570:Risk Assessment; D018675:Toxicity Tests / Q000379:methods*
BMD approach; BMR 1 SD; CES; Effect size; benchmark response; critical effect size; dose-response data; maximum effect; natural variability; within-group variation