Of course there is cherry-picking throughout, it is rather boringly obvious. The anonymous authors of the website simply pick the most convenient values for their analysis so that they can have a better looking model regardless of severity etc
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I identified one example of this in the thread, but it's pretty much ubiquitous throughout the analysis
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Replying to @GidMK @K_Sheldrick
Can you name the author in the example of cherry-picking? I don't see it. Which study was misplaced in the inclusion-exclusion criteria?
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Replying to @EduEngineer @K_Sheldrick
It's not the inclusion criteria, which are basically "chuck all the awful studies into one website". It's just that the authors extract only "positive" results regardless of whether studies actually showed a benefit
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Replying to @GidMK @K_Sheldrick
You keep conflating "didn't show a benefit" with "wasn't statistically significant", but the latter doesn't make a difference in a binary p-value computation.
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And your idea of "number wasn't corrected properly" means "awful study" is probably only slightly correlating in reality. I've read a 5-digit number of studies, and I find such mistakes in the majority of them, at every level of clinical quality.
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Replying to @EduEngineer @K_Sheldrick
Yes as I said there are a lot of awful studies. This one is particularly worthless tho, for a number of reasons not limited to the one that you want to ignore for some reason
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Replying to @GidMK @K_Sheldrick
I'm not ignoring it. I stated that it was worth contacting the author and figuring it out, but before that step, it's not a reason to suggest "worthless".
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Replying to @EduEngineer @K_Sheldrick
That's one element of quality. It's not even the only thing that I pointed out in this thread. Other issues are the lack of reporting of potential confounders, the inadequate control group, the lack of reporting generally, the contradictions btwn pre-reg and study...
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Replying to @GidMK @K_Sheldrick
Unless there is a reason to believe confounders skew one direction, magically, missed confounders are understood not to have a significant effect on such computed p-values. In fact, that's the point of a p-value: it only asks the question of whether the results happen at random.
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By definition a confounder skews in one direction, otherwise it does not confound the relationship and is not a confounder
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And this is a huge and fundamental issue with the study - we don't even know if the control group had similar ages! Wild stuff
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