Comment by mitthrowaway2
3 years ago
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.
Isn't this just the standard challenge of separation of signal and noise that exists in all disciplines, though? It seems that it would be better dealt with by general Bayesian methods than by strict adherence to a controlled-trial process that requires ignoring or even discarding large quantities of data (including every patient who ever had the disease prior to the start of the trial, which for rare diseases, might even be the majority of the data!)
Bayesian approaches alone are not enough to establish causality. I say this as someone using bayesian approaches in a causal analysis. Causality is intricately linked to the processes that underly how data is generated. Two different processes can lead to the same probabilistic results.
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> It seems that it would be better dealt with by general Bayesian methods than by strict adherence to a controlled-trial process that requires ignoring or even discarding large quantities of data
What is the Bayesian prior on "After receiving the COVID vaccination, patient died when they were struck by lightning?"
This is what I mean when I say the 'clean room' practices that are standard for most other fields of science would be considered unethical for medical trials. You can't, generally, lock people in a box for months to eliminate other confounding factors when trialing medicine; the best approach we have, therefore, is to minimize confounding factors by minimizing variables in space, time, &c. Yes, if we could lab-rat humans, keep them in controlled environments perpetually, we could use the previous in-a-box state as a comparison to the current state, probably. That's obviously a non-starter.
(There are some categories of medical research where time-variance has been used; it's just not generally an acceptable signal source, IIUC, for drug and therapy trials. If for no other reason than not nearly enough information has been captured about unrelated past patients to be compared to a current trial because relevant information to monitor was not known when monitoring past patients).
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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.