Dave J wrote:A Bayesian way of thinking about this stuff is what's needed, but experimenters avoid it because it exposes the flaws in "the system". If you ask at the beginning of a mouse trial "what is the probability that this will produce information that will be useful in development of effective human ALS therapy?", well, that's been a lot more than 22 mouse trials! Mouse trials collectively fail the P=.05 test because the way they're being done, they're the wrong kinds of trials for developing effective human ALS therapy.
Dave J.-
I think you are misinterpreting the 1 in 22 comment from R.A. Fisher. He was stating his convenient choice for liking P=0.05 for a cutoff was based on it coinciding with a differerce very close to 2 standard deviations from the mean, and that meant the expectation would be that at that low P value, 1 in 22 trials could be concluded a real effect. It was stated in 1925, when people had to look up statistics in tables, and so the correlation with 2 standard deviations was somewhat a matter of convenience. To him, though, it represented a reasonable risk. This 1 in 22 has absolutely nothing to do with translation to humans, as the P value calculation is simply a mathematical tool to compare two datasets to conclude if they are different.
A far as the other question considering history, the probability would be zero, or very low if you consider Rilutek to apply. But, even if the tool is blunt and has historically low translation success, I don't know if we can blame only the tool. I am optimistic that it is also related to the difficult nature of ALS, and us not testing the right therapy. Think of it this way: Should we also throw away human clinical trials of ALS because of the lack of success?
Wayne-
You caught me. I was just trying to make a point, that the evolved definition of statistical significance is somewhat arbitrary. Many think we should stop using 5% and simply state the confidence level for others to decide for themselves, if it is statistically significant. Especially so, when we now, 86 years later, have computers to calculate this quickly. The value should always be given, not a simple mention of which side of the line. How close was it??? (e.g. Apocynin discussions).
For us PALS, with our lives on the line, I am just saying that the line shouldn't be so absolute. I suspect some even forget the real meaning. P=0.06 and P=0.04 are really close, yet one happens to fall on the right side of a threshold. I don't know for sure that ALSTDI decided to abandon just because of the p value. But, just as you infer a lot regarding something like statements and not data, I am doing the same regarding the many instances of reading "was not statistically significant". And, when I know statistical significance is VERY effected by sample size, I would hope that those that are close to the cutoff can have a repeat (or enlarged) test to see if the difference is REAL. This gets back to whether the trials are even powered sufficiently to ever have small P values with a small difference. I would love to calculate this myself, and am only missing one input to do so, standard deviation of mouse survival. Certainly ALSTDI has this data and could quickly tell us the power to detect various size effects with their standard test protocol.
This statistical power and sample size concern is partially fueled by this:
From Supplementary Table 4 of the Nature Genetics Report:
Additionally, all studies were undertaken using the criteria delineated in Scott et al. 2008 which indicates a group size of N=20 or 24 has over 90% power to detect a 15% effect, >85% power to detect a 10% effect.To me, this implies they were expecting to see larger differences, using numbers like 15% effect and 10% effect. I realize their actual sample size may be larger (unless we segregate genders). Given the history, it would seem more relevant to discuss the power to detect 1.5%, 3% and 5% effects.
BTW- Can we call a truce, my friend? I'll concede that you can make it go higher. LOL
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