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Andrew Althouse
@ADAlthousePhD
, 4 tweets, 1 min read Read on Twitter
One of the challenging things about teaching Intro Stats and, specifically, linear regression, is trying to get across the idea that two variables need not be perfectly correlated for their relationship to be useful or meaningful.
IMO, a lot of beginning statistics students see a scatterplot with a fair amount of spread, then see a significant p-value and get confused: how can there be evidence of a "linear association" between the variables? The correlation is only 0.3!
But if one variable explains 10% of the variation in another variable, that information can be really important! It doesn't mean that I can predict Y from X by itself, but in biomedical research, that need not be the case for a relationship to be useful.
(this story doesn't really have an ending, it's just kind of a musing about teaching linear regression to beginning stat students)
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Andrew Althouse
@ADAlthousePhD
JOB ALERT: if you are an aspiring trial statistician (or an experienced trial statistician seeking a change of scenery) that would consider a move to Pittsburgh, feel free to DM me for details.
Background in stats, biostats, epi, or any related field, but clinical trial experience (student experience counts) is highly preferred. The group doing the hiring is specifically looking for someone with interest in trials.
Tossing this out to #statstwitter and #epitwitter for a signal boost.
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Andrew Althouse
@ADAlthousePhD
Good passage from the guidelines of reporting for clinical research in urology (ncbi.nlm.nih.gov/pubmed/30537407):
This is a common fallacy: within a group of patients that got a treatment, showing that a specific risk factor is associated with poorer outcome, and concluding that patients with that risk factor are therefore poor candidates for that treatment.
...in doing so, failing to appreciate that the treatment still may have improved the outcome for those patients (vs. them not getting the treatment).
Read 5 tweets
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Andrew Althouse
@ADAlthousePhD
Question for folks about a research "design" that I don't like very much, but I see often enough to wonder how much I'm being a hater:
An investigator want to see whether (Intervention) reduces the incidence of (Outcome). They don't actually have a comparison arm available, just data on patients that received the intervention (for whatever reason).
So instead of testing the incidence of(Outcome) in the (Intervention) against control over the same period of time, they report the outcomes in the X months before starting the intervention against the X months after the intervention.
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(thread) Yesterday Crytek filed their rebuttal to the motion to dismiss (MtD) which RSI/CIG filed on Jan 5th, amid much fanfare.

You can read it in all its apocalyptic glory on Pacer or at the link below.

docdroid.net/v7yQ0LL/respon…
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threadreaderapp.com/thread/9496260…
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