Thanks, Doctor❤

This raises many questions, the most important of which is whether academics can discuss a logical question and one can admit a mistake. ITT is upheld by some (including Wikipedia) and considered completely wrong by others (like me: Wikipedia would not accept my edits). You can discuss experimental situations and describe the appropriate statistical approach but as stated ITT is fundamentally idiotic. I had suggested that we can be explicit by recognizing that ITT ask the question (as stated in the video) What is the effect of ASSIGNING a drug or intervention? Most readers do not want to know this but if they do, then you can do ITT. However, it must be stated explicitly. And consistently --many articles describe an intervention as assigning patients to adding coconut oil to their food but the article quickly morphs to a study of the effect of coconut oil even paper and let the press say coconut oil is bad for you, that's wrong. Incorrect. If you think there was a placebo effect you have to show that. "May" is not data. It is not science. In the real world, you don't know who took the drug so you must effectively due ITT but we always did that and we don't need a special name. We attribute the effect to the coconut oil because that's the best we can do. When we find out about compliance, we have to do something different. The real world is separate from the intervention. Surgery will have a different effect if it is carried out at Mass General or on a battlefield (God willin').. The real point: 1. ITT requires that if nobody takes the pill, then you must say that the pill has the effect that you measure in these subjects. 2. If the subject told you before the experiment that they cannot, for religious reasons or whatever, take a pill, you would exclude them from the study. Finding out after you start, doesn't change anything. 3. Randomization refers to relevant variables that you are not testing. You intend to break the randomization by measuring response to a new variable. ITT is foolish and should never be done (if you know the details of adherence) unless you emphasize that it is about the intention. (The road to statistical hell is paved...) So, one of us is wrong. One us has to admit a mistake. I am willing if you can answer the objections above. Are you up for resolving this issue? Admittedly, I have the advantage in that I do experimental biochemistry and make two or three mistakes a week which I have to face up to. So, what say you?

but ITT also dilute the treatment effect, especially in trials with high drop outs and deviation from portocol

I like this research because every medical opinion cites statistics in the belief that facts = truth, but the conversations go "this study says ___, that study says ___". We are all very much in the same boat by trusting our arbitrary decisions regardless of how "healthy" we may be. kzbin.info/www/bejne/fIncao1smJWjgqc

Thank you for this video Dr Shaughnessy, it was helpful to visualize through your specific example why allocation concealment might introduce bias

Thank you very much. Things are clear now.