Morning all!
For those of us interested..
Here's a crash course on Bayesian Hypothesis Testing - Click Here!
Some key takeaways (basic ones!)..
1) Accounting for new information helps update beliefs, to test hypotheses.
So it almost seems like, in all reality, you could start from the first patient who responds whether positively or not positively and then build from there.
2) The probability of a hypothesis that's met is as important as the probability that its not met
From what I gather, simplistically, it would seem the Bayesian probability predicates on the probability of ___X__ happening given __Y__
Now, after some thought, I think we can all agree that X would be 'Day 28 OR'.
What do you think Y might be?
Overwhelming efficacy - the stats required, page-260
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