Again: the probability that a drug has no specific, detectable effects is NOT zero.
I don’t care about detectability when I take a drug. I care about whether it helps me.
I want a number that tell me the probability of the drug helping me. I don’t want the statisician to answer a different question.
Detectability depends on the power of a trial.
If a frequentist gives you some number after he analysed an experiment you can’t just fit that number in a decision function.
You have to think about issues such as whether the experiment had enough power to pick up an effect.
If a bayesian gives you a probability you don’t have to think about such issues because the bayesian already integrates your prior knowledge. The probability that the bayesian gives you can be directly used.
Drug trials are neither designed to, nor capable of answering questions like this.
Whether a drug will help you is a different probability that comes out of a complicated evaluation for which the drug trial results serve as just one of the inputs.
If a bayesian gives you a probability you don’t have to think about such issues
Whether a drug will help you is a different probability that comes out of a complicated evaluation for which the drug trial results serve as just one of the inputs.
That evaluation is in it’s nature bayesian. Bayes rule is about adding together different probabilities.
At the moment there no systematic way of going about it. That’s where theory development is needed. I would that someone like the FDA writes down all their priors and then provides some computer analysis tool that actually calculates that probability.
I am sorry, you’re speaking nonsense.
If the priors are correct then a correct bayesian analysis provides me exactly the probability in which I should believe after I read the study.
Again: the probability that a drug has no specific, detectable effects is NOT zero.
Huh? What? I don’t even… Please quote me.
What do you call an “actionable” probability? What would be an example of a “non-actionable” probability?
I don’t care about detectability when I take a drug. I care about whether it helps me. I want a number that tell me the probability of the drug helping me. I don’t want the statisician to answer a different question.
Detectability depends on the power of a trial.
If a frequentist gives you some number after he analysed an experiment you can’t just fit that number in a decision function. You have to think about issues such as whether the experiment had enough power to pick up an effect.
If a bayesian gives you a probability you don’t have to think about such issues because the bayesian already integrates your prior knowledge. The probability that the bayesian gives you can be directly used.
Drug trials are neither designed to, nor capable of answering questions like this.
Whether a drug will help you is a different probability that comes out of a complicated evaluation for which the drug trial results serve as just one of the inputs.
I am sorry, you’re speaking nonsense.
That evaluation is in it’s nature bayesian. Bayes rule is about adding together different probabilities.
At the moment there no systematic way of going about it. That’s where theory development is needed. I would that someone like the FDA writes down all their priors and then provides some computer analysis tool that actually calculates that probability.
If the priors are correct then a correct bayesian analysis provides me exactly the probability in which I should believe after I read the study.