Language for communicating frequentist results about treatment effects

[R_cubed]

I found Royall’s evidential perspective via likelihood very helpful. Michael Lew does as well.

Mayo’s critique is that error control is missing from the analysis, and we need some design info for that. But it seems that error control is a function of the experimental design (before data point of view), where we fix the effect to a specific value. It does not seem relevant after data have been collected, as long as we assume the experiment was minimally informative (1-\beta > \alpha).

A post data view is interested in how the observed data supports a range of effects, above and below the value used in the design phase. This would seem to be related to your post on Bayesian power.

Bayesian Power: No Unobservables

Most statisticians I’ve spoken with who frequently collaborate with investigators in doing frequentist power calculations believe that the process is nothing more than a game or is voodoo. Minimal clinically important effect sizes are typically re-thought until budget constraints are met, and investigators frequently resort to achieving a power of only 0.8, i.e., are happy to let type I error be 4\times the type I error probability. As a study progresses, the problem re-emerges when doing co…

Perhaps it would be better to look at inference as precise estimation, rather than Mayo’s concept of “severe tests.”

I just thought Lew’s paper was a useful link between what people currently use now, and how they related to a better measure (likelihoods) and hoped to get some expert input on that.