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Re:Causal inference as a blind spot of data scientists by lr1970 on Sunday October 15, 2023 @04:36PM (#37893182)
There can be a real ethical dilemma when applying A/B testing in medical setting. Placing someone with an incurable disease in a control group is condemning them to death while in treatment group they might have a chance. On the other hand, without a proper A/B testing methodology the drug efficacy cannot be established. So far no perfect solution to the dilemma has been found.
Re:Causal inference as a blind spot of data scientists by bertil on Sunday October 15, 2023 @05:47PM (#37893693)
All the alternative methods require the same sacrifice. More importantly, most suggested treatments fail to cure deadly conditions or have major side effects or risks that are just as unethical to thrust upon people untested.

If you look at it properly, i.e. evaluate what should be your actions before the test (Do nothing, Impose untested treatment, Test with proper control to learn what to do with the majority of the population), the answer is rarely ambiguous.

There is a debate to be had on how much pre-clinical work to be done before clinical testing, but those are increasingly automated, cheap, and fast, so we often reach the point where a double-blind test is the next logical step.

The argument you present is based on either an unwarranted confidence in treatments, or information that wasn’t available when the decision had to be made.

Re:Causal inference as a blind spot of data scientists by hobs on Sunday October 15, 2023 @09:03PM (#37894863)
It's not really an ethical dilemma until you know it works, and then usually if the evidence is strong enough they'll cut the trial early.
Re:Causal inference as a blind spot of data scientists by jrumbut on Monday October 16, 2023 @01:00AM (#37896081)
> in a control group

The control group gets the current standard treatment, not nothing (in case that was a source of confusion). Plus they typically don't have to pay for it which is a benefit for them.

Large trials today will typically conduct interim analyses and will have pre-defined guidelines for when to stop the trial because the new treatment is either clearly providing a benefit or is clearly futile.

Here is an example of such a study: https://www.ahajournals.org/doi/10.1161/CIRCHEARTFAILURE.111...

Re:Causal inference as a blind spot of data scientists by twobitshifter on Monday October 16, 2023 @06:18AM (#37897777)
You can end the trial early when it’s clear the treatment is working. This just happened last week with Ozempic for diabetes caused kidney disease. https://www.wxyz.com/news/health/ask-dr-nandi/novo-nordisk-e...
Re:Causal inference as a blind spot of data scientists by najarvg on Monday October 16, 2023 @11:43AM (#37901449)
Most therapeutic trials are nowadays "Intent to treat". So subject would receive either standardized tx or experimental tx in th e randomization. Many of them also have crossovers such that when measurable (as defined by the protocol) benefit is seen, standard tx based subjects can be moved over to the experimental arm