“Our proposal is a new type of paper for animal studies (…) that incorporates an independent, statistically rigorous confirmation of a researcher’s central hypothesis.” (p.409)
A comment tribune in Nature of Feb 23, 2017, suggests running clinical trials in three stages towards meeting higher standards in statistical validation. The idea is to impose a preclinical trial run by an independent team following an initial research showing some potential for some new treatment. The three stages are thus (i) to generate hypotheses; (ii) to test hypotheses; (iii) to test broader application of hypotheses (p.410). While I am skeptical of the chances of this proposal reaching adoption (for various reasons, like, what would the incentive of the second team be [of the B team be?!], especially if the hypothesis is dis-proved, how would both teams share the authorship and presumably patenting rights of the final study?, and how could independence be certain were the B team contracted by the A team?), the statistical arguments put forward in the tribune are rather weak (in my opinion). Repeating experiments with a larger sample size and an hypothesis set a priori rather than cherry-picked is obviously positive, but moving from a p-value boundary of 0.05 to one of 0.01 and to a power of 80% is more a cosmetic than a foundational change. As Andrew and I pointed out in our PNAS discussion of Johnson two years ago.
“the earlier experiments would not need to be held to the same rigid standards.” (p.410)
The article contains a vignette on “the maths of predictive value” that makes intuitive sense but only superficially. First, “the positive predictive value is the probability that a positive result is truly positive” (p.411) A statement that implies a distribution of probability on the space of hypotheses, although I see no Bayesian hint throughout the paper. Second, this (ersatz of a) probability is computed by a ratio of the number of positive results under the hypothesis over the total number of positive results. Which does not make much sense outside a Bayesian framework and even then cannot be assessed experimentally or by simulation without defining a distribution of the output under both hypotheses. Simplistic pictures are the above are not necessarily meaningful. And Nature should certainly invest into a statistical editor!