A general theory of effect size, and its consequences for defining the benchmark response (BMR) for continuous endpoints. (21st April 2017) - Slob, Wout
Abstract: 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.