Comment by mitthrowaway2
3 years ago
The people who have already died to-date of your terminal illness aren't a good enough control group to tell that your cure works? This is the absurdity of frequentist statistics. If you can't tell the difference between recovering from the treatment and recovering by natural luck, then either the treatment is barely effective at all, or the doctor had no basis to tell you in the first place that you had a terminal illness.
That is correct, the people that have already died are unable to act as effective controls. To establish the medicine causes the disease to go away, you have to use something like a randomized trial. Otherwise, causation is not established. It’s a very difficult problem and a lot of smart people have try to come up with better systems.
With all due respect, why? This seems fallaciously wrong.
It is rather akin to saying that I can't tell whether seatbelts reduce car-crash fatalities simply by comparing collision fatalities from 1967 seatbelt-lacking car models with otherwise-identical 1968 seatbelt-equipped models; I would need to actually randomly and secretly sabotage a certain percentage of the seatbelts from 1968-model cars, while deliberately ignoring everything I know about prior fatality statistics. Even if every automobile collision without seatbelts was fatal, and every collision after seatbelts became mandatory was survived, even if nothing about the cars or driving conditions had changed, you're implying that it wouldn't provide any evidence of the efficacy of seatbelts, unless we conducted a new trial from scratch with a randomized control group as though we had no prior data?
What is the actual difference, in terms of causal evidence, between someone who died of the same disease before the study officially begins, and someone in the "control group" who dies after the study begins?
Almost nothing about medicine operates nearly as cleanly as that example; that's one of the major challenges with medical science. The seatbelt analogy breaks down in this context because the hypothetical of "Even if every automobile collision without seatbelts was fatal, and every collision after seatbelts became mandatory was survived, even if nothing about the cars or driving conditions had changed" doesn't reflect medical reality (and in the rare instances where it does, i.e. when improvement for the test group becomes obvious, immediate, and apparently side-effect free, it can become morally imperative to discontinue the control group).
In medicine, you get confounding factors like change in standard of care year-to-year and facility-to-facility. You almost never get the kind of "clean-room" trials you get in mechanical engineering (and, indeed, the processes to create them would often themselves be unethical). So the best you usually get is trying to reason around (to torture the analogy a bit) "Well we introduced seatbelts three years ago and relative to the year prior to that, fatalities went way down... Oh wait, but COVID also happened the same year we introduced seatbelts and everyone stopped driving" if you compare a current trial to past non-trial standard care as the control group.
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The process in which a treatment is administered might change the way people behave, changing outcomes. Trials with control groups are easier to justify as having avoided these issues.
If you're interested in learning more, I would recommend learning about Judea Pearl's do-calculus, specifically the backdoor criterion. Although it isn't the end-all of causal analysis, it is a very useful non-experimental approach that will help you develop an appreciation for the benefits of controlled experiments.
As far as seat belts go, that's actually been something discussed a bit in economics. The main concern is if adding seat belts changes how people drive (confounding the actual effects of seat belts). The solution was to measure the protective effect of seat belts on occupants of the passenger seat in collisions involving drunk drivers.
People who already died are less effective controls than double-blind controls getting a placebo, but they're not ineffective.
Given a treatment with a large effect size like penicillin, a causal link will be very easy to establish even with a suboptimal control group.
If a disease at a certain well defined stage has a 100% fatality rate after 6 months, why would you need a double blind trial if some patients are still alive 1 year after taking your drug ?
The causality is pretty clear and while you might not claim that it double the life expectancy it would IMHO be immoral not to fast track the approval of that drug.