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Kevin

@Impossible_PhD doesn't surprise me at all, the emotional / psychological effects were so rapid for me. Obviously facial hair etc took longer but I felt different almost immediately.

24 comments
Doc Impossible

@KevinLikesMaps Oh, absolutely 0% surprise here. It almost perfectly mirrors the non-RCT results we've had, which is what you'd expect to see in good science. The literal *only* weakness in the study is sample size. With a total corpus of 67, we're short of the sizes we'd need to *really* tear apart the data for potential interference and such, but recruiting corpus pf, say, 500 transmascs experiencing SI and seeking immediate HRT in a specific geographical area is a very tall order.

Jennifer Kayla | Theogrin 🦊

@Impossible_PhD @KevinLikesMaps
Could probably scare up that many in a large university city like KWC, but even with the projected rates of 1% or so (I do think 5% is a bit pessimistic), there are also the troubles of finding those who aren't already on HRT and willing to engage in such a study. We're consistently in a Catch-22 bind.

SO many eggs I wish could be helped...

Doc Impossible

@theogrin @KevinLikesMaps Let's do math here with NYC, population 8.4m.

1.6% are trans, and 20% of that are trans men (20% are trans women, 60% are nonbinary). That's 26,880 trans men in NYC. Of those, you need *only* those who are having SI (~40%), so 10,752. Of that, you need the folks who want HRT (~33%), for 3,548. Of THOSE, you need the guys who have not yet begun HRT, but have decided to get it. Then, only those who are willing to do an RCT.

That is a SMALL population, isn't it?

Jennifer Kayla | Theogrin 🦊

@Impossible_PhD @KevinLikesMaps
Not as small as it sounds, but also there's the question of outreach, of finding the population...

It's amazing they found as many as they did. I certainly don't scour the corkboard at the local market on a regular basis.

Doc Impossible

@theogrin @KevinLikesMaps Yeah, for a big sample you'd need a nationwide study for sure.

Jennifer Kayla | Theogrin 🦊

@Impossible_PhD @KevinLikesMaps
Even then...

We are implicitly distrustful. We have to be. Our lives demand it. We've been burned time and again.

In a city of 2m people, 70 is the most we can honestly hope for.

How fucked is that?

Jennifer Kayla | Theogrin 🦊

@Impossible_PhD @KevinLikesMaps
Shit, fuck and damn, it's the Fermi paradox writ domestically.

Doc Impossible

@theogrin @KevinLikesMaps yeeeeeep. Percentages of percentages of percentages of percentages do that *really* quickly.

Jennifer Kayla | Theogrin 🦊

@Impossible_PhD @KevinLikesMaps

Intellectually, I know this full well.

One of the problems with being human, though, is that we have zero scope of scale.

Tiny little bacterium can't hurt us, suddenly we're all dead of Black Death.

Lions, how terrible! Mosquitoes, meh.

And so on. Smol things multiply... and get ignored.

A 1% chance also becomes a certainty in time, and people don't grok how often they roll the dice.

Dovekie

@Impossible_PhD Hello! I've seen that 60/20/20 breakdown in numbers of trans people before -- I'm curious, where are those numbers coming from in this case? I would like to add them to my notes :D thank you

Faith has purple hair! :v_tg: :v_lb:

@Impossible_PhD While the study certainly needs to be repeated, the sample size isn't that bad given how definitive the results are. One of the funny things about statistics is that you really only need a sample size of 30-35 to get very precise results IF it's actually a random sampling. That's a load-bearing "if", though. Double-blind should endure that the two groups are randomly selected from within the test group as a whole. The bigger question is if the group that was desperate enough to sign up for HRT under those circumstances are representative of the trans-masc community at large. I'm not the right person to answer that question so I won't speculate.

Still, it's a spectacular and encouraging result. Hopefully other research groups and other hospitals will repeat the experiment. Then hopefully regulators will listen and just give the trans dudes their T when they ask for it. 🤞

@KevinLikesMaps

@Impossible_PhD While the study certainly needs to be repeated, the sample size isn't that bad given how definitive the results are. One of the funny things about statistics is that you really only need a sample size of 30-35 to get very precise results IF it's actually a random sampling. That's a load-bearing "if", though. Double-blind should endure that the two groups are randomly selected from within the test group as a whole. The bigger question is if the group that was desperate enough to sign...

Doc Impossible

@faithisleaping @KevinLikesMaps Oh yeah. I've done my own work with randomized samples, so definitely the case--it's just that the difference in strength between a 67 participant study and a 670 participant study is much more than an order of magnitude, in terms of confidence.

Faith has purple hair! :v_tg: :v_lb:

@Impossible_PhD Actually no it's not. That was kinda my point. For what the study is trying to show—a reduction in mental suicidal ideation in trans-mascs when they start T—you don't actually need a big sample size. You could run that study with 100k participants and it wouldn't directly increase statistical confidence. No, that doesn't appear to make sense on the face of it but it's true. Statistics can be surprising sometimes.

If you are asking a question in the form "If I do X on a Y, does Z happen (vs. an appropriate null)?" where you can quickly and reliably observe Z, you don't need a big sample size. You need an actual random sample of Y—which is sometimes easier said than done. In fact, "OMG look at my sample size!" is one of the classic tools used to lie about statistics. A crap study with a million participants is still crap. A good study with 50 is better.

So why did drug company run 50k-person tests on COVID vaccines? Why not run them on a few dozen and call it good? Two reasons:

1.

Safety. The study size required to prove it works isn't huge (still bigger than 35 but a few thousand will do). The real reason for huge studies is to try and catch the rare side effects. No only are 1:1000 side effects possible but you need enough data to determine whether that side effect is actually from the vaccine or from eating cucumbers for lunch. In order to ferret that out, you need the 1:1000 side effect to be repeated a bunch of times. That means a lot of people.


2.

Unless you're going to actually inject people with COVID—which would be highly unethical—you need to wait until people get it randomly. Given that a single person may never get COVID over the space of a year if they're reasonably careful—which most early vaccine trial candidates probably were—waiting for subjects to get randomly exposed takes a long time with a small sample size. You get more people exposed randomly the more people you have in your study so it goes faster.

The study wasn't going for safety so that removes point 1. To 2, when testing the effect of T on suicidal trans-mascs, you can just ask. It's important to note that it removed ideation, not completion of suicide. That's something you can test for just by asking and get rapid and accurate data. If you were testing for completion, you would fall into case 2 and need a bigger study.

(Sorry if I'm going into math educator mode here. I've seen too much crap about statistics in the last 3-4 years. 😫)

@KevinLikesMaps

@Impossible_PhD Actually no it's not. That was kinda my point. For what the study is trying to show—a reduction in mental suicidal ideation in trans-mascs when they start T—you don't actually need a big sample size. You could run that study with 100k participants and it wouldn't directly increase statistical confidence. No, that doesn't appear to make sense on the face of it but it's true. Statistics can be surprising sometimes.

Book

@Impossible_PhD @KevinLikesMaps that's already 30 people who needed T and were given a placebo, sure they agreed to that, but I do not think getting a better sigma is worth putting more people into that shitty situation

Doc Impossible

@book @KevinLikesMaps I'm not saying we should--just that that's the only possible axis for criticism.

Jess👾

@Impossible_PhD @KevinLikesMaps I'm also a little surprised it would pass an IRB. Taking a person who reports SI and giving him placebos rather than T seems ethically ick.

Kevin

@JessTheUnstill @Impossible_PhD I must be missing something or bad at math, but I don't think all of the participants had SI.
according to the article: 64 participants (in each group? not clear to me)
11 had resolved SI after HRT - but then they say that's 52%, so that must be only 21 people who had SI to begin with.

Kevin

@JessTheUnstill @Impossible_PhD it'd be good to have a link to the actual study, but I don't have the exec function to hunt it down right now (I'm not asking you to do the work, just explaining)

Anatoly

@KevinLikesMaps @Impossible_PhD came here to say the same thing. Within two days the backdrop of negative static in my brain of like passive suicidality was just…gone. Completely gone.

jocanib

@KevinLikesMaps @Impossible_PhD

Not surprising but need to see the full report. The numbers here don't add up. Sample size 64 but 11 people is 52% of the T group (implying 21 in the group) and 1 person is 5% of the control group (implying 20 people in that group). What happened to the other 23?

There's also no placebo control, which is pretty critical for the credibility of subjective outcomes. Looks like a weak study design, unfortunately.

Doc Impossible

@jocanib @KevinLikesMaps I mean, that's just not accurate. At least half of the total cohort was the plan ebo control. That's how you do an RCT; you can't call it an RCT without a 50% placebo control. That's literally that the RC means.

jocanib

@Impossible_PhD @KevinLikesMaps

Half the total cohort is 32 but they report only 20 on control and 21 on T. What happened to the other 23?

Doc Impossible

@jocanib @KevinLikesMaps we'll find out when the full study gets published. This is a report from a conference presentation

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