The point is that we have conditioned on non-random selection into the sample. That is, collider bias is induced by selection into the dataset. This is an extreme example, but it nicely demonstrates how observed associations in-sample may not hold more broadly/out of sample.
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Results from samples that are likely not representative of the target population should be treated with caution by scientists and policy makers, as naive interpretation of results could lead to public health decisions that fail or even cause unintentional harm.
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Finally, I do not claim ownership of this. It was the brainchild of
@explodecomputer and a massive team effort from@Garethjgriffith,@tudballstats@AnnieSHerbert Giulia Mancano@Lindsey_Pike@ammegandchips Tom Palmer,@mendel_random Kate Tilling@Luisa_Zu@nm_davies and myselfShow this thread -
Any comments, criticism or suggestions would be gratefully received and extremely useful
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@ammegandchips@Lindsey_Pike and I have also tried to summarise the paper in an accessible way here: https://ieureka.blogs.bristol.ac.uk/2020/05/10/collider-bias-why-its-difficult-to-find-risk-factors-or-effective-medications-for-covid-19-infection-and-severity/ …Show this thread
End of conversation
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