A research area with a great deal of uncertainty but potentially high payoff is anti-ageing medicine. But how good is it to put more resources into?
To be concrete, let’s look at the problem of being able to stop a majority of the ageing processes in cells. Let’s:
Measure R(0) (current resources for the area) in $
Measure B (annual benefits) in QALYs
Take p = 0.2
So the estimate for y/z should be how many times historical efforts to solve the problem we’ll need before there’s a 20% total chance of success.
I think this is a particularly uncertain problem in various ways: our error bars on estimates are likely to be large, and the model is not a perfect fit. But it’s also a good example of how we might begin with really no idea about how cost-effective we should think it is, and so produce a first number which can be helpful.
R(0): The SENS Foundation has an annual budget of around $4m, plus extra resources in the form of labour. Stem cell research has a global annual budget probably in the low billions, although it’s not all directly relevant. Some basic science may be of relevance, but this is likely to be fairly tangential. Overall I will estimate $1b here, although this could be out by an order of magnitude in either direction.
B: Around 100m people die every year. It’s unclear exactly what the effects of success would be on this figure, but providing a quarter of them with an extra 10 years of life seems conservative but not extremely so. So I estimate 250m QALYs/year
y/z: Real head-scratching time. I think 10 times historical resources wouldn’t get us up to a 20% chance of success, but 10,000 times historical resources would be more than enough. I’m going to split the difference and say 300.
A research area with a great deal of uncertainty but potentially high payoff is anti-ageing medicine. But how good is it to put more resources into?
To be concrete, let’s look at the problem of being able to stop a majority of the ageing processes in cells. Let’s:
Measure R(0) (current resources for the area) in $
Measure B (annual benefits) in QALYs
Take p = 0.2
So the estimate for y/z should be how many times historical efforts to solve the problem we’ll need before there’s a 20% total chance of success.
I think this is a particularly uncertain problem in various ways: our error bars on estimates are likely to be large, and the model is not a perfect fit. But it’s also a good example of how we might begin with really no idea about how cost-effective we should think it is, and so produce a first number which can be helpful.
My estimates.
R(0): The SENS Foundation has an annual budget of around $4m, plus extra resources in the form of labour. Stem cell research has a global annual budget probably in the low billions, although it’s not all directly relevant. Some basic science may be of relevance, but this is likely to be fairly tangential. Overall I will estimate $1b here, although this could be out by an order of magnitude in either direction.
B: Around 100m people die every year. It’s unclear exactly what the effects of success would be on this figure, but providing a quarter of them with an extra 10 years of life seems conservative but not extremely so. So I estimate 250m QALYs/year
y/z: Real head-scratching time. I think 10 times historical resources wouldn’t get us up to a 20% chance of success, but 10,000 times historical resources would be more than enough. I’m going to split the difference and say 300.