The urgency of relieving severe physical pain reveals the serious limitations of the “QALYs gained” approach to measuring scale of impact. A person with terminal cancer treated with morphine for two months might remain highly disabled and in a very poor state of health, and gain only a fraction of a QALY, yet be spared two months of agony.

I'm not sure I follow this. QALYs allow negative values, so if morphine treatment increased health-related quality of life from, say, -0.5 to +0.1, it would gain 0.6 QALYs per year. Most/all currently-used value sets would give less weight than that to pain relief, but I don't think that's primarily because of their other health states. Extending that life would gain few QALYs, but that doesn't seem to be your concern.

That said, it might depend on the method used to combine utility decrements for various health states. The most common approach to dealing with comorbidities is to multiply utility decrements, e.g. if the decrement for cancer is 0.4 and for pain is 0.6, you'd end up with (0.4*0.6) = 0.24 (assuming a baseline/counterfactual of full health). Maybe that's what you were getting at?

Note that there are also methods for calculating confidence intervals around ICERs that avoid issues with ratios. The best I'm aware of is by Hatswell et al. I have an Excel sheet with all the macros etc set up if you want.

MAICER = maximum acceptable incremental cost-effectiveness ratio. This is often called the willingness to pay for a unit of outcome, though the concepts are a little different. It is typically represented by lambda. 

The CE plane is also useful as it indicates which quadrant the samples are in, i.e. NE = more effective but more costly (the most common), SE = more effective and cheaper (dominant), NW  = less effective and more costly (dominated), and SW =  less effective and cheaper. When there are samples in more than one quadrant, which is very common, confidence/credible intervals around the ICER are basically meaningless, as are negative ICERs more broadly. Distributions in Guesstimate, Causal, etc can therefore be misleading.

The standard textbook for heath economic evaluation is Drummond et al, 2015, and it's probably the best introduction to these methods.

For more details on the practicalities of modelling, especially in Excel, see Briggs, Claxton, & Sculpher, 2006.

For Bayesian (and grudgingly frequentist) approaches in R, see stuff by Gianluca Baio at UCL, e.g. this book, and his R package BCEA.

Cost-effectiveness planes are introduced in Black (1990). CEACs, CEAFs, and value of information are explained in more detail in Barton, Briggs, & Fenwick (2008); the latter is a very useful paper.

For more on VOI, see  Wilson et al., 2014  and Strong, Oakley, Brennan, & Breeze, 2015.

For a very clear step-by-step explanation of calculating and interpreting ICERs and net benefit, see Paulden 2020. In the same issue of PharmacoEconomics there was a nice debate between those who favour dropping ICERs entirely and those who think they should be presented alongside net benefit. (I think I'm in the latter camp, though if I had to pick one I'd go for NB as you can't really quantify uncertainty properly around ICERs.)

For an application of some of those methods in EA, you can look at the evaluation we did of Donational. I'm not sure it was the right tool for the job (a BOTEC + heuristics might have been as good or better, given how speculative much of it was), and I had to adapt the methods a fair bit (e.g. to "donation-cost ratio" rather than "cost-effectiveness ratio"), but you can get the general idea. The images aren't showing for me, though; not sure if it's an issue on my end or the links are broken.

Here is a more standard model in Excel I did for an assignment.

Hope that helps. LMK if you want more. 

This is a recognised issue in health technology assessment. The most common solution is to first plot the incremental costs and effects on a cost-effectiveness plane to get a sense of the distributions:

Then to represent uncertainty in terms of the probability that an intervention is cost-effective at different cost-effectiveness thresholds (e.g. 20k and 30k per QALY). On the CEP above this is the proportion of samples below the respective lines, but it's generally better represented by cost-effectiveness acceptability curves (CEACs), as below:

Often, especially with multiple interventions, a cost-effectiveness acceptability frontier (CEAF) is added, representing the probability that the optimal decision (i.e. the one with highest expected net benefit) is the most cost-effective.

I can dig out proper references and examples if it would be useful, including Excel spreadsheets with macros you can adapt to generate them from your own data (such as samples exported from Guesstimate). There are also R packages that can do this, e.g. hesim and bcea.

For traditional QALY calculations, researchers simply ask people how they feel when experiencing certain things (like a particular surgery or a disease) and normalize/aggregate those responses to get a scale where 0 quality is as good as death, 1 is perfect health, and negative numbers can be used for experiences worse than death.

This isn't correct. QALY weights are typically based on hypothetical preferences, not experiences.

What Richard described is more like a WELBY, which has a similar structure but covers wellbeing in some sense rather than just health. See Part 1 of my (unfinished) sequence on this if you're interested.

Glad you found it useful. I am not qualified to comment on the role of neuron count in sentience; you may want to look at work by Jason Schukraft and others at Rethink Priorities on animal sentience and/or get in touch with them.

If you haven't already, you may also want to review the 2018 Humane Slaughter Association report, which was the best I could find in early 2019. While looking for it, I also just came across one from Compassion in World Farming, which I don't think I've read.

On fish, there were several comments here, including this one from me.

The 2018 Humane Slaughter Association report was probably the best info available at the time; not sure what's happened since. 

There are also easy-access savings accounts giving a bit more than 1.3%: https://www.moneysavingexpert.com/savings/savings-accounts-best-interest/

There is a lot of potential in fish welfare/stunning. In addition to what others have mentioned, IIRC from some reading a few years ago:

•  The greatest bottleneck in humane slaughter is research, e.g. determining parameters/designing machines for stunning each major species, as they differ so much. There just aren't many experts in this field, and the leading researchers are mostly very busy (and pretty old), but perhaps financial incentives would persuade some people with the right sort of background to go into this area.
• As well as electrical and percussive stunning, anaesthetising with clove oil/eugenol seems a promising and under-researched method of reducing the pain of slaughter.  Because it may just involve adding a liquid/powder to a tank containing the fish, it may also require less tailoring to each species than than other methods (though it can affect the flavour if "too much" is used). I have some notes on this if anyone is interested.
• Crustastun could be mass-produced and supplied cheaply/freely to places that would otherwise boil crustaceans alive. I seem to recall a French lawyer had invented another machine that was even better (or cheaper) but was too busy to promote it; maybe EAs could buy the patent or something?