First of, I want to acknowledge that discussing this issues is indeed very difficult. I’m happy that you made it through whatever you had to go through (I could qualify this experience, but I expect any effort on my side to fall short of being helpful), and I’m immensely sorry that you had to face all these different issues, in lack of a better term. I also want to pre-emptively say that I share some of your critiques and don’t want to come off as judging your experience.
However, I have some questions on my mind. I’ll just leave one here, in the hope that it doesn’t come off as insensitive.
I’d be curious to see how switching from QALYs to something else would re-order EA priorities. What would your guess be ? Would SWD plausibly be above e.g. malaria prevention?
I’m not requesting anything extremely specific or committed, but I think it would help me paint a more complete picture of the critique, and potentially identify clearer points of disagreement.
Thank you for raising this question, it is certainly not insensitive. Feel free to ask more questions.
I am also wondering how switching from QALYs would change EA priorities. My guess is that it depends entirely on the weights in the model. I want to do some comparisons with different alternatives to see how they would inform priorities. Some alternatives I would like to test are:
DCEA, distributional cost-effectiveness analysis;
MCDA, multi-criteria decision analysis;
ECEA, extended cost-effectiveness analysis;
EBW, equity-based weighting;
MP, mathematical programming;
stratified CEA;
from the studies mentioned in this article. I’m not very clued up on alternative CEA measurements, so I was hoping someone more knowledgeable would mention an alternative.
Once I have done that analysis, I’ll post a follow up to this post and that would clear up that confusion. It’s not something that will happen quickly though.
The point of this post was to explain gaps in current measurements of health outcomes from the point of view of the tangible day-to-day effects, as well as how what is being measured often doesn’t match reality. I’m not an expert in mathematical models or health economics but I am an expert in being chronically ill, so that’s the lens I was offering.
One guess is that doing away with negative QALYs would mess with animal welfare calculations because a lot of them rely on negative QALYs. However, if I play devil’s advocate, it could be argued that animals should get a very high weight in terms of historical disenfranchisement, in which case, the calculations would change but I suspect animal welfare would still be one of the top issues.
Thanks for this posting this.
First of, I want to acknowledge that discussing this issues is indeed very difficult. I’m happy that you made it through whatever you had to go through (I could qualify this experience, but I expect any effort on my side to fall short of being helpful), and I’m immensely sorry that you had to face all these different issues, in lack of a better term. I also want to pre-emptively say that I share some of your critiques and don’t want to come off as judging your experience.
However, I have some questions on my mind. I’ll just leave one here, in the hope that it doesn’t come off as insensitive.
I’d be curious to see how switching from QALYs to something else would re-order EA priorities. What would your guess be ? Would SWD plausibly be above e.g. malaria prevention?
I’m not requesting anything extremely specific or committed, but I think it would help me paint a more complete picture of the critique, and potentially identify clearer points of disagreement.
Thank you for raising this question, it is certainly not insensitive. Feel free to ask more questions.
I am also wondering how switching from QALYs would change EA priorities. My guess is that it depends entirely on the weights in the model. I want to do some comparisons with different alternatives to see how they would inform priorities. Some alternatives I would like to test are:
DCEA, distributional cost-effectiveness analysis;
MCDA, multi-criteria decision analysis;
ECEA, extended cost-effectiveness analysis;
EBW, equity-based weighting;
MP, mathematical programming;
stratified CEA;
from the studies mentioned in this article. I’m not very clued up on alternative CEA measurements, so I was hoping someone more knowledgeable would mention an alternative.
Once I have done that analysis, I’ll post a follow up to this post and that would clear up that confusion. It’s not something that will happen quickly though.
The point of this post was to explain gaps in current measurements of health outcomes from the point of view of the tangible day-to-day effects, as well as how what is being measured often doesn’t match reality. I’m not an expert in mathematical models or health economics but I am an expert in being chronically ill, so that’s the lens I was offering.
One guess is that doing away with negative QALYs would mess with animal welfare calculations because a lot of them rely on negative QALYs. However, if I play devil’s advocate, it could be argued that animals should get a very high weight in terms of historical disenfranchisement, in which case, the calculations would change but I suspect animal welfare would still be one of the top issues.