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.
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.