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GidMK's profile
Health Nerd
Health Nerd
Health Nerd
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@GidMK

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Health NerdVerified account

@GidMK

Epidemiologist. Writer (Guardian, Observer etc). "Well known research trouble-maker". PhDing at @UoW Host of @senscipod Email gidmk.healthnerd@gmail.com he/him

Sydney, New South Wales
theguardian.com/profile/gideon…
Joined November 2015

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    1. Andrew Althouse‏ @ADAlthousePhD 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      When a caliper is used, two patients with similar but not identical scores can be matched to one another. But if you require an *exact* match of the PS, that can almost only be achieved with patients that have the same covariate values used to compute the PS.

      1 reply 0 retweets 5 likes
    2. Andrew Althouse‏ @ADAlthousePhD 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      Unless two *covariates* in the PS model have exactly the same regression coefficient (in which case you could match a 67 year old smoker without diabetes to a 67 year old nonsmoker with diabetes - if smoking & DM had **exactly** the same regression coefficient in the PS model)...

      1 reply 0 retweets 4 likes
    3. Andrew Althouse‏ @ADAlthousePhD 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      ...but if each variable included in the PS model has a unique value of the regression coefficient, then the only “exact matches” on the PS will be patients that have exactly the same covariate values.

      1 reply 0 retweets 3 likes
    4. Andrew Althouse‏ @ADAlthousePhD 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      This paper has plenty of things to critique, but I do not believe this is a copy-paste error nor is it fraudulent (at least, not based on this column of this table). In fact I would be more concerned if *exact matches* on the PS did *not* look like this.

      1 reply 1 retweet 7 likes
    5. Andrew Althouse‏ @ADAlthousePhD 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      Andrew Althouse Retweeted Eric Weinhandl

      As @eric_weinhandl pointed out here: https://twitter.com/eric_weinhandl/status/1280630034894479360?s=21 …https://twitter.com/eric_weinhandl/status/1280630034894479360 …

      Andrew Althouse added,

      Eric Weinhandl @eric_weinhandl
      Replying to @eric_weinhandl @SteveJoffe
      OTOH, maybe that is the explanation? The authors found only 96 (of 1985) HCQ-exposed patients with PS values that were exactly equal to HCQ-unexposed patients. These 192 patients occupied cells with perfectly equal covariate vectors—indeed, all covariates are binary.
      1 reply 0 retweets 4 likes
    6. Andrew Althouse‏ @ADAlthousePhD 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      I’m literally in the hospital with my wife and a newborn right now, otherwise I would make up a simulated dataset and R code to try and illustrate this. If you want to do it yourself, generate a dataset with a bunch of binary covariates, then...

      16 replies 2 retweets 58 likes
    7. Andrew Althouse‏ @ADAlthousePhD 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      ...run a propensity score matching algorithm with a caliper distance of 0, and examine the matched pairs you end up with. They’ll probably all be perfect matches on every covariate the included in the PS model.

      1 reply 0 retweets 8 likes
    8. Andrew Althouse‏ @ADAlthousePhD 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      (The fact that the authors did this raises other red flags, most notably that no one really uses propensity score matching this way, just clarifying that the exact match of the two columns is oddly enough consistent with what they said they did)

      2 replies 1 retweet 10 likes
    9. JH // from fairest tweeps we desire increase‏ @jhan2qt 7 Jul 2020
      Replying to @ADAlthousePhD @MarkHoofnagle and

      Agree. They used 21 dichotomized (!) characteristics for propensity score matching. Plus treatment, that yields 2^22 possible combinations. It's extremely unlikely to find 190 perfect matches in a finite real world sample, except when the variables are highly correlated.

      2 replies 0 retweets 0 likes
    10. JH // from fairest tweeps we desire increase‏ @jhan2qt 7 Jul 2020
      Replying to @jhan2qt @ADAlthousePhD and

      By rule of thumb the number of matches would be poisson distributed with a lambda of less than 11.9 (=2500^2/2^19). So 190 matches are unlikely.

      1 reply 0 retweets 0 likes
      Health Nerd‏Verified account @GidMK 7 Jul 2020
      Replying to @jhan2qt @ADAlthousePhD and

      But in this sample, the variables are highly correlated e.g. BMI, cardiovascular complications, age, kidney disease etc. Makes it less unlikely, especially when everything is dichotomized

      8:39 PM - 7 Jul 2020
      • 1 Like
      • JH // from fairest tweeps we desire increase
      1 reply 0 retweets 1 like
        1. New conversation
        2. JH // from fairest tweeps we desire increase‏ @jhan2qt 7 Jul 2020
          Replying to @GidMK @ADAlthousePhD and

          Thanks. This gave me more insight into criticism of the PS method. The algorithm finds more matches in certain subgroups, potentially biasing the results.

          1 reply 0 retweets 1 like
        3. JH // from fairest tweeps we desire increase‏ @jhan2qt 8 Jul 2020
          Replying to @jhan2qt @GidMK

          Update: I have written a quick and dirty simulation and even for low intra-item correlations 190 or more matches were achieved frequently.

          0 replies 0 retweets 1 like
        4. End of conversation

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