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bristimtom's profile
Tim Morris
Tim Morris
Tim Morris
@bristimtom

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Tim Morris

@bristimtom

Research Fellow at @MRC_IEU. Health, education, genes, social inequality, geography and stats. Also do a bit of cycling.

Bristol
bit.ly/2VOzBdb
Joined October 2014

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    1. Tim Morris‏ @bristimtom 10 May 2020

      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.

      2 replies 15 retweets 152 likes
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    2. Tim Morris‏ @bristimtom 10 May 2020

      How does this relate to coronavirus? Well, many of the current COVID-19 datasets rely on non-random participation with strong selection pressures. We present a few different examples in the preprint to highlight this (there are many more!).pic.twitter.com/iEUHMSK0mE

      2 replies 47 retweets 203 likes
      Show this thread
    3. Tim Morris‏ @bristimtom 10 May 2020

      These selection pressures can throw up some weird associations and may be partly responsible for some of those already observed such as smoking appearing protective (https://www.medrxiv.org/content/10.1101/2020.05.06.20092999v1 …; https://www.qeios.com/read/WPP19W.3 ) and ACE-inhibitors appearing harmful (https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3583469 …)

      4 replies 28 retweets 121 likes
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    4. Tim Morris‏ @bristimtom 10 May 2020

      We ran some simulations under a similar scenario to one of the smoking papers demonstrating how 2-fold protective/risk effects of smoking on COVID-19 infection could appear due to selection (no true underlying causal relationship).pic.twitter.com/wICHmHuIhh

      4 replies 16 retweets 101 likes
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    5. Tim Morris‏ @bristimtom 10 May 2020

      We also looked at how 2,556 traits in @uk_biobank associate with being tested for COVID-19 (note this is being tested, NOT a positive test result). 32% of the traits associated with testing (false discovery rate of <0.05). The QQ plot here shows the enrichment for associations. npic.twitter.com/jSnfz12idy

      2 replies 6 retweets 65 likes
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    6. Tim Morris‏ @bristimtom 10 May 2020

      So in the UK Biobank, lots of things strongly associate with having received a COVID-19 test, and can therefore be expected to associate with each other within this sub-sample even where they do not in the wider cohort or the wider UK population (note the double selection here).

      2 replies 11 retweets 66 likes
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    7. Tim Morris‏ @bristimtom 10 May 2020

      Are these observed associations true or the result of collider bias? Well, it's difficult to tell from the data, but there are some sensitivity analyses that can be run. These largely depend on the data available on non-participants though.

      1 reply 2 retweets 48 likes
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    8. Tim Morris‏ @bristimtom 10 May 2020

      The only way to be completely confident that collider bias doesn't underlie observed associations is to use representative population samples with random participation and attrition patterns. This is obviously unlikely, so using sampling strategies that minimise the problems...

      1 reply 11 retweets 93 likes
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    9. Tim Morris‏ @bristimtom 10 May 2020

      of collider bias and doing everything we can to ensure that samples are as representative and as free from strong selection pressures as possible is really important. Beyond this, we must do our best to hold in mind the likely selection pressures when interpreting (COVID-19) data

      2 replies 6 retweets 71 likes
      Show this thread
    10. Tim Morris‏ @bristimtom 10 May 2020

      @Garethjgriffith also created a brilliant Shiny app (available at http://apps.mrcieu.ac.uk/ascrtain/ ) where you can play around with various selection parameters to look at collider bias.

      3 replies 14 retweets 114 likes
      Show this thread
      Tim Morris‏ @bristimtom 10 May 2020

      So in summary, there is potential for collider bias when using highly-selected samples to investigate risk factors for COVID-19 infection/severity. It is of vital importance that associations between variables of interest are interpreted in light of this.

      5:22 AM - 10 May 2020
      • 24 Retweets
      • 132 Likes
      • Hwa Shi-Hsia (IPA: /xwa/ /ʂɚ/ /ɕja/) x I A I Ʌ x L \ I V Ram Fernando J Kepler Astartae Johan Van Weyenbergh Marco D'Agostini Cindy Malinga Fernando
      1 reply 24 retweets 132 likes
        1. New conversation
        2. Tim Morris‏ @bristimtom 10 May 2020

          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.

          3 replies 21 retweets 104 likes
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        3. Tim Morris‏ @bristimtom 10 May 2020

          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 myself

          3 replies 4 retweets 84 likes
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        4. Tim Morris‏ @bristimtom 10 May 2020

          Any comments, criticism or suggestions would be gratefully received and extremely useful

          16 replies 2 retweets 44 likes
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        5. Tim Morris‏ @bristimtom 10 May 2020

          @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/ …

          7 replies 27 retweets 128 likes
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        6. End of conversation

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