5/n One of the first things I do when I read randomized trials like this is look at their pre-registration. This is basically what the authors SAID they would do registered BEFORE they did it Here's the pre-reg for this trial:https://clinicaltrials.gov/ct2/show/NCT04429711?term=NCT04429711&draw=2&rank=1 …
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16/n Also worth noting that it is entirely possible the study still has some positive results when you add these patients back in - the point is their primary outcome, which is the main thing reported, seems to change substantially when these people are included!
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Seems you're being overly kind here
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Agree that by our current definitions this is unlikely to be 'misconduct' but this probably means our definitions need to be improved. At least they gave you enough info to analyse according to the original protocol.
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Declaring changes from the pre-registration would seem like the minimum we should expect.
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Hey, thanks for sharing this thread from
@GidMK. Read the whole version here:https://threader.app/thread/1435128867027501056 …
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While, as an epidemiologist, this may be basic due diligence, for a layperson (aka me) or non-scientist that's probably a little bit out of reach: even if only because we don't know what issues we're looking for. Do you have any tips on how to spot 'iffy' claims like this?
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Unfortunately you can't know if they've made changes after the study if you don't check the trial preregistration record/it isn't preregistered. In this case the >35Ct criterium raises eyebrows, so would flag a check, but you 'should' check every trial anyway...
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Article was sumbmitted on May 2021, as of today it has not gotten past peer review?
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To me this looks like motivated reasoning. They probably did the trials as pre-registered and saw no difference. So they tried to make it work somehow; Then they decided if they could 'just' exclude the patients that tested positive early into the trial they would 1/
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get the 'wanted' result. They probably told themselves: 'the early positives cannot be counted because IVM didn't have time to work' or similar to justify that. Obviously, with a small cohort of 116, the targeted removal of 21 that are asymettrically spread over any time 2/
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