3 ms·

I feel this is a really reasonable take. This study doesn’t say anything bad about colonoscopy, it says that it is possible the effects are not only due to the

I feel this is a really reasonable take. This study doesn’t say anything bad about colonoscopy, it says that it is possible the effects are not only due to the colonoscopy so it is probably between 0 and claimed effect effective. Can’t really say where in the range from the study.

em500 4y ago
Yup. The study answers a question from health policy makers: Should we invite everyone (in some age group) for a colonoscopy? Based on this study, probably not. It does not answer questions from individuals: Should I get a colonoscopy? If you have some good reason (symptoms, doctor advice, family history), probably yes (based on other studies, not this one).
- sokoloff 4y ago
  To the extent that there’s a spread in those answers, a third possibility emerges: how can we, as health policy makers, find a way to make the invitations more effective at converting to administered colonoscopies?
  - thaumasiotes 4y ago
    Why would that be a goal? First you'd need to show that colonoscopies were helpful in general, or that invitations were going to a subpopulation in which colonoscopies were helpful.
    - sokoloff 4y ago
      If colonoscopies are helpful to avoid negative outcomes, but invitations to colonoscopies are not, looking into making invitations better seems like an obvious play to me.
      - JamesBarney 4y ago
        The problem is you can't run a study on colonoscopies that doesn't involve an invitation without forcing people to get a colonoscopy at gun point. Which would both be illegal and never pass an IRB.
        sokoloff 4y ago
        You can control for the invitation vs no-invitation and colonoscopy vs no-colonoscopy and analyze the outcomes of all four cells (provided you have enough people in each cell) or the column or row independently.
        thaumasiotes 4y ago
        You can do that, but you won't learn anything, because the populations within each cell are not comparable to each other.
        sokoloff 4y ago
        It seems to me with electronic medical records that you could do population-wide studies using data that already exists (and I think that would pass IRB, or at least "ought to"). That would likely tell you "for the patients matching criteria X (are covered by BCBS and live in state X, or whatever), the 10 year outcome for patients who turned 50 in 2010 was Y vs Z conditioned on whether they had a colonoscopy".
        thaumasiotes 4y ago
        What would you be trying to learn from that? You're comparing unlike groups. You can't draw any conclusions.
        sokoloff 4y ago
        If I look at all people born in 1960, living in NY, covered by BCBS, that’s a like group. If I then split by “had a colonoscopy between 2008 and 2012, inclusive” vs “didn’t” and look at 2012 through 2021 outcomes to draw conclusions, it’s possible that that filtering makes them unlike groups (I mean, it definitionally does in at least the primary selection criteria). Given that the effort is approximately that of a SQL query, I’d be interested to know if there’s a possible signal there, which would need to be corroborated with other data sets to determine the repeatability of the correlation and then if there’s any likely causal link.

em500 4y ago

Yup. The study answers a question from health policy makers: Should we invite everyone (in some age group) for a colonoscopy? Based on this study, probably not. It does not answer questions from individuals: Should I get a colonoscopy? If you have some good reason (symptoms, doctor advice, family history), probably yes (based on other studies, not this one).

sokoloff 4y ago
To the extent that there’s a spread in those answers, a third possibility emerges: how can we, as health policy makers, find a way to make the invitations more effective at converting to administered colonoscopies?
- thaumasiotes 4y ago
  Why would that be a goal? First you'd need to show that colonoscopies were helpful in general, or that invitations were going to a subpopulation in which colonoscopies were helpful.
  - sokoloff 4y ago
    If colonoscopies are helpful to avoid negative outcomes, but invitations to colonoscopies are not, looking into making invitations better seems like an obvious play to me.
    - JamesBarney 4y ago
      The problem is you can't run a study on colonoscopies that doesn't involve an invitation without forcing people to get a colonoscopy at gun point. Which would both be illegal and never pass an IRB.
      - sokoloff 4y ago
        You can control for the invitation vs no-invitation and colonoscopy vs no-colonoscopy and analyze the outcomes of all four cells (provided you have enough people in each cell) or the column or row independently.
        thaumasiotes 4y ago
        You can do that, but you won't learn anything, because the populations within each cell are not comparable to each other.
        sokoloff 4y ago
        It seems to me with electronic medical records that you could do population-wide studies using data that already exists (and I think that would pass IRB, or at least "ought to"). That would likely tell you "for the patients matching criteria X (are covered by BCBS and live in state X, or whatever), the 10 year outcome for patients who turned 50 in 2010 was Y vs Z conditioned on whether they had a colonoscopy".
        thaumasiotes 4y ago
        What would you be trying to learn from that? You're comparing unlike groups. You can't draw any conclusions.
        sokoloff 4y ago
        If I look at all people born in 1960, living in NY, covered by BCBS, that’s a like group. If I then split by “had a colonoscopy between 2008 and 2012, inclusive” vs “didn’t” and look at 2012 through 2021 outcomes to draw conclusions, it’s possible that that filtering makes them unlike groups (I mean, it definitionally does in at least the primary selection criteria). Given that the effort is approximately that of a SQL query, I’d be interested to know if there’s a possible signal there, which would need to be corroborated with other data sets to determine the repeatability of the correlation and then if there’s any likely causal link.