Full text here:https://onlinelibrary.wiley.com/doi/full/10.1002/ptr.6328 …
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The authors randomly assigned participants with type 2 diabetes to receive either curcumin (n=23) or placebo (n=21) for 10 weeks.
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Participants gave a baseline and end-of-trial blood sample from which the research team measured triglycerides, total cholesterol, LDL, HDL, hs-CRP, and adiponectin at both time points.
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The study team did some things properly in carrying out this trial. Unfortunately, they did not use a very good approach for their data analysis and reporting of results.
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For the moment, let’s put aside multiplicity considerations and focus on just one outcome: triglycerides, since a) it’s listed at the top of Table 4 and b) the authors chose to highlight triglyceride (TG) results in their abstract and conclusions.
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Replying to @ADAlthousePhD
Let's also ignore a study of diabetes that took bloods but didn't include a measure of blood sugar????
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Replying to @GidMK
Yeah, I’m staying in “my lane” and commenting on the statistical issues here, but feel free to point out any other problems. I don’t think this trial should be taken very seriously, but it’s a good teaching vehicle to explain why NOT to do what they did for analysis.
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Replying to @ADAlthousePhD @GidMK
This is what you get when people that don’t know how to properly analyze RCT’s perform one without statistical support.
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Replying to @ADAlthousePhD
I keep seeing this exact issue so I really appreciated the thread! Can't believe how often people do this weird analysis that basically ruins their entire RCT
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Replying to @GidMK
Ugh, so obviously I’m biased as a statistician, but this is the BS that makes us statisticians say things like “you should never do an RCT without a statistician involved” - this is a major and easily correctable mistake.
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I think this is one area where your bias only adds weight to your opinion 
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