See post #64 for a full description of the model.
Ideally, yes. But in reality even late-stage and other confirmatory studies are designed for a limited number of primary outcomes. Despite the regulated context lots of ad-hoc analyses are performed both on primary and secondary outcomes, even though from a purely theoretical point of view they should be considered worthless. That is clearly not the case.
In an ideal world every experiment would be properly designed and planned for every single outcome. But one needs to recognize that this is not the reality. Interesting things have been learned from badly designed experimental setups, studies without a formal statistical analysis plan, even from weird lab accidents etc. At the very least new hypotheses will be generated. The goal of a statistical analysis is to enable a decision under uncertainty - it needs to be good enough, it does not need to be perfect (and it will never be perfect).
Experiments are always performed in a context, in this case the Harman studies. One needs to look at evidence in this wider context, maybe perform a meta-analysis if you want to be quantitative. If you're still not convinced you can now design your own confirmatory experiment thanks to
@MatthewS's hard work. He has shared all the data you need to come up with an appropriate design, model and sample size requirements. I hope that this work serves as an inspiration for other members.