So your example of decades being wasted chasing an initial tiny effect size, all the time, was... An example of a failed mechanistic hypothesis that wasn't based on a tiny effect size.
I wasn't trying to post about the effect size specifically, but about general incentives and dead ends, but if you want a specific example look no further than aspirin for myocardial infarction:
> A commonly cited example of this problem is the Physicians Health Study of aspirin to prevent myocardial infarction (MI).4 In more than 22 000 subjects over an average of 5 years, aspirin was associated with a reduction in MI (although not in overall cardiovascular mortality) that was highly statistically significant: P < .00001. The study was terminated early due to the conclusive evidence, and aspirin was recommended for general prevention. However, the effect size was very small: a risk difference of 0.77% with r2 = .001—an extremely small effect size. As a result of that study, many people were advised to take aspirin who would not experience benefit yet were also at risk for adverse effects. Further studies found even smaller effects, and the recommendation to use aspirin has since been modified.
Long-term aspirin use has its own risks, like GI bleeds, and the MI benefits are clearly not warranted given those risks.
It's hard to parse that example, because the citation it contains is to a meta-analysis that provides and effect size of aspirin for MI in the PHS in the form of an odds ratio that is much greater magnitude. Digging a bit more, heres the actual result - the difference in relative risk was 44% not 0.77%.
https://www.nejm.org/doi/full/10.1056/NEJM198907203210301
> There was a 44 percent reduction in the risk of myocardial infarction (relative risk, 0.56; 95 percent confidence interval, 0.45 to 0.70; P<0.00001) in the aspirin group (254.8 per 100,000 per year as compared with 439.7 in the placebo group).
I agree if you said from the start you meant general incentives, especially in pharma development, but that is by and large a different conversation.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444174/
Quote:
> A commonly cited example of this problem is the Physicians Health Study of aspirin to prevent myocardial infarction (MI).4 In more than 22 000 subjects over an average of 5 years, aspirin was associated with a reduction in MI (although not in overall cardiovascular mortality) that was highly statistically significant: P < .00001. The study was terminated early due to the conclusive evidence, and aspirin was recommended for general prevention. However, the effect size was very small: a risk difference of 0.77% with r2 = .001—an extremely small effect size. As a result of that study, many people were advised to take aspirin who would not experience benefit yet were also at risk for adverse effects. Further studies found even smaller effects, and the recommendation to use aspirin has since been modified.
Long-term aspirin use has its own risks, like GI bleeds, and the MI benefits are clearly not warranted given those risks.