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Well, the final report of the Cass Review just dropped. It's getting coverage in mainstream publications like the BBC. Surprising no one who paid attention to the interim report, it concludes that there is insufficient evidence in the realm of trans healthcare for children:
Critics are already jumping on the fact that the report used the GRADE approach to categorize evidence, which only allows randomized control studies to be classified as "high quality of evidence" and which can drop non-blinded studies one level in assessed quality, thus preventing many non-blinded studies from qualifying as high quality evidence. (Bold is edit added later. See ArjinFerman's response below, and my response - original GRADE standards can be found here.) The critics point out that double-blinded randomized control studies just aren't possible in some areas of medicine. For a simple example, if the intervention is something like "cosmetic breast augmentation", then there's logically no sensible control group - since there's no placebo that can make people believe they got bigger breasts when they didn't. (It's worth pointing out that this criticism of GRADE isn't unique to trans activists. The Wikipedia page for GRADE mentions it is criticized in general when it comes to slowly progressing diseases like atherosclerosis, where observational studies are easier to perform than RCTs.)
As a result of the GRADE approach, we read things like this in the report:
My own opinion is that I can partially agree with Cass that I want to see higher quality studies around trans healthcare for children in general, but I think that her methodology (using GRADE) is of the sort that will always say we "don't have enough high quality studies", and so her arguments don't have legs to stand on. A problem I see a lot in studies is using some "industry standard" for investigating a topic, and coming to a result of some kind, but failing to justify why the "industry standard" was the best thing to use here. In a better version of the Cass Review, I would have liked to see a few paragraphs justifying the use of GRADE, and explaining why they used this standard and not some other standard.
I mean, isn't that a thing good scientific reports in general do at all steps of the process? Think of what a critic would claim about your model and methodology, and then explain why your model or methodology is the best one to use in this particular instance. Show that your findings are robust even if you used some slightly different model or methodology, and explain what conditions are necessary for your model or methodology to fail. A quick search through the Cass Review shows that it doesn't seem to have done this. It just used GRADE, didn't really justify the decision, and didn't discuss alternatives or why its arguments are robust under alternative assumptions about the data.
It's a bit circular to arbitrarily use a standard that will say, "there are basically no high quality studies in this medical field" no matter what, and then to conclude in your recommendations to the government, "We need more high quality studies before we do anything more in this medical field!"
You will sometimes see a medical study described as a "blinded randomized controlled trial" and other medical studies as an "open label randomized controlled trial". Whether a study is blinded or open-label is a separate issue from having a control group and separate again from having random assignment to treatment. The Wikipedia page on GRADE doesn't mention blinding. Checking the website of the National Institute for Clinical Excellence (NICE) the reference to randomized controlled trials does not mention blinding.
A study on breast augmentation can qualify as a randomized controlled trial, generating top quality evidence, with the following design: recruit 200 subjects. Randomly assign 100 to have the operation. The others are the control group. Researchers must keep in touch with the all 200 to find out how things worked out for them.
Keeping in touch with all 200 might be tricky. Some of those in the trial group might be disappointed with the results of the surgery and feeling disillusioned with medical intervention may reject further contact with the trial. Some of those in the control group may interpret being rejected for surgery as being rejected from the trial and disappear. Such people are lost to follow up. How to analyse a trial with large loses to follow up is controversial. Do we blandly say "we don't know"? Should we interpret losses from the trial group as bad outcomes? One might vary the design. 100 breast augmentations. 50 get psychotherapy that aims to persuade them that they don't need breast augmentation. 50 get regular contact to keep in touch, but bland contact, merely reminding then that they also serve who only stand and wait.
Scott's deep dive into Alcoholics Anonymous is my goto article for the practical importance of having a control group. Or should that be the disappointing effect of control groups?
If a trial does not have randomization, it is vulnerable to Simpson's Paradox. One may find that a medical treatment is beneficial, but partitioning the data into two exhaustive and mutually exclusive subgroups, find that the treatment is harmful to one of the subgroups and also harmful to the other subgroup. Wut? The analysis may collapse into baffling incoherence. Actually it is worse than that. The laws of arithmetic are chaotic evil, and permit that a conclusion that has been reversed by one partition may yet be swapped back by a finer one (if the Chrome browser objects to a faulty certificate, using incognito mode will work.)
The two issues, of needing a control group, and of needing randomization, are widely understood; I would not expect Dr Hilary Cass to restate the arguments in her report.
Edited to fix link to Simpsons Paradox, spotted error way too late :-(
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