5 ms·

The other leading theory is that major (non emergent) operations and management changes (like starting chemotherapy) will often be postponed until the senior ph

The other leading theory is that major (non emergent) operations and management changes (like starting chemotherapy) will often be postponed until the senior physicians get back.

taeric 6y ago
That sounds easily testable in the data.
- Cpoll 6y ago
  I suspect that's harder to collect. Deaths are probably public/census data, but details about individual cases are subject to confidentiality.
  - yellowapple 6y ago
    Should be possible to anonymize the data, no?
    - Cpoll 6y ago
      I'm sure it is, but it's more difficult than just collecting mortality numbers, and therefore not easy to control for.
  - throw_away 6y ago
    Wouldn't we be able to see the count of operations and management changes go down during these periods and up immediately afterwards if they were being delayed?
    - Cpoll 6y ago
      I suspect specialists are already booked to capacity, and they don't play catch-up after a conference, it's just planned into the schedule.
      - sukilot 6y ago
        They rate returns to normal after the conference related drop.
- 6gvONxR4sf7o 6y ago
  It's only testable in the data if these decisions are being made based on factors that are adequately represented in your statistics (no unobserved confounding). Maybe there's patient A, a smoker who says they're a non-smoker but seems like a liar to the doctor, and an otherwise identical patient B who is actually a non-smoker. Patient A is probably higher risk, so there's a difference between doing A during conference week then B later, versus B during conference week than A later. Even supposing that the doctor wrote that they think A might be lying and actually maybe a smoker, it's going to be hard to control for those notes in a t-test. It's a shitty example, but hopefully demonstrates the idea. It's always super hard to make the argument that there's no unobserved confounding.
  - deleted 6y ago
    [deleted]
  - Retric 6y ago
    Conference dates are independent of patient heath, so you can just look at the numbers directly across many healthcare systems and several decades. Vastly more data generally trumps better analysis.
    - 6gvONxR4sf7o 6y ago
      > Vastly more data generally trumps better analysis. If you mean more data in the sense of more covariates, sure. If you mean more data in terms of just more observations, no way. Not when it comes to causal inference.
      - lowdose 6y ago
        Could you elaborate on this point "no way", for more observations?
        6gvONxR4sf7o 6y ago
        Think about getting a helicopter to the hospital. It's probably pretty serious, right? You're more likely to die if you get a helicopter evac than if you don't. If all you have recorded is whether they got to the hospital via helicopter or not, and whether they died or not, then you're going to see that helicopters are associated with death. If you could control for what's wrong with the person, you might see that helicopters save lives. If you don't have that data, then just having more observations of (helicopter y/n, died y/n) will just look like helivac is murder. No matter what you do with that data, you just can't control for what you'd need to, because what you need was an unobserved confounder. When you're trying to establish causation, you have to rule out unobserved confounders, which is tricky. There's always something where someone might say "well what if it was X?" and the data doesn't contain that answer. In this particular case, that commenter said that conference dates are independent enough from patient health to say that aside from the important things, everything else is equal. As in, the only difference between the treatments is who is doing the treatment. I disagree with that assertion, but that's the kind of argument you need for causation: Once we've dealt with/controlled for XYZ, the only difference left is the one we're interested in. It's very difficult to demonstrate that the only difference left is what you care about when you can't even see certain variables. Someone says "what if people in group A are more likely to be left handed and it's patient handedness instead of doctor quality that's causing death here" but you didn't measure left handedness. More and more observations of group A without measuring handedness can't rule out that maybe they were lefties. So you either measure handedness, or argue that your setup will even it out (via randomization for example), or argue that handedness just doesn't matter here. And not just for handedness, but for everything imaginable.

The other leading theory is that major (non emergent) operations and management changes (like starting chemotherapy) will often be postponed until the senior physicians get back.

taeric 6y ago

That sounds easily testable in the data.

Cpoll 6y ago
I suspect that's harder to collect. Deaths are probably public/census data, but details about individual cases are subject to confidentiality.
- yellowapple 6y ago
  Should be possible to anonymize the data, no?
  - Cpoll 6y ago
    I'm sure it is, but it's more difficult than just collecting mortality numbers, and therefore not easy to control for.
- throw_away 6y ago
  Wouldn't we be able to see the count of operations and management changes go down during these periods and up immediately afterwards if they were being delayed?
  - Cpoll 6y ago
    I suspect specialists are already booked to capacity, and they don't play catch-up after a conference, it's just planned into the schedule.
    - sukilot 6y ago
      They rate returns to normal after the conference related drop.
6gvONxR4sf7o 6y ago
It's only testable in the data if these decisions are being made based on factors that are adequately represented in your statistics (no unobserved confounding). Maybe there's patient A, a smoker who says they're a non-smoker but seems like a liar to the doctor, and an otherwise identical patient B who is actually a non-smoker. Patient A is probably higher risk, so there's a difference between doing A during conference week then B later, versus B during conference week than A later. Even supposing that the doctor wrote that they think A might be lying and actually maybe a smoker, it's going to be hard to control for those notes in a t-test. It's a shitty example, but hopefully demonstrates the idea. It's always super hard to make the argument that there's no unobserved confounding.
- deleted 6y ago
  [deleted]
- Retric 6y ago
  Conference dates are independent of patient heath, so you can just look at the numbers directly across many healthcare systems and several decades. Vastly more data generally trumps better analysis.
  - 6gvONxR4sf7o 6y ago
    > Vastly more data generally trumps better analysis. If you mean more data in the sense of more covariates, sure. If you mean more data in terms of just more observations, no way. Not when it comes to causal inference.
    - lowdose 6y ago
      Could you elaborate on this point "no way", for more observations?
      - 6gvONxR4sf7o 6y ago
        Think about getting a helicopter to the hospital. It's probably pretty serious, right? You're more likely to die if you get a helicopter evac than if you don't. If all you have recorded is whether they got to the hospital via helicopter or not, and whether they died or not, then you're going to see that helicopters are associated with death. If you could control for what's wrong with the person, you might see that helicopters save lives. If you don't have that data, then just having more observations of (helicopter y/n, died y/n) will just look like helivac is murder. No matter what you do with that data, you just can't control for what you'd need to, because what you need was an unobserved confounder. When you're trying to establish causation, you have to rule out unobserved confounders, which is tricky. There's always something where someone might say "well what if it was X?" and the data doesn't contain that answer. In this particular case, that commenter said that conference dates are independent enough from patient health to say that aside from the important things, everything else is equal. As in, the only difference between the treatments is who is doing the treatment. I disagree with that assertion, but that's the kind of argument you need for causation: Once we've dealt with/controlled for XYZ, the only difference left is the one we're interested in. It's very difficult to demonstrate that the only difference left is what you care about when you can't even see certain variables. Someone says "what if people in group A are more likely to be left handed and it's patient handedness instead of doctor quality that's causing death here" but you didn't measure left handedness. More and more observations of group A without measuring handedness can't rule out that maybe they were lefties. So you either measure handedness, or argue that your setup will even it out (via randomization for example), or argue that handedness just doesn't matter here. And not just for handedness, but for everything imaginable.