Tag Archives: death

stop talking about the “death state” in DCEs

I feel like a broken record here – sorry in advance for those who already knew this.

Another paper has:

(1) Talked about putting “Death” in as a state to anchor DCE estimates to get proper QALY values, (although thankfully they didn’t do it in their study, but even saying it is a possible solution is wrong)

(2) Not done a proper literature review. I, together with Tony Marley (who, together with Duncan Luce, axiomatized random utility theory independently of McFadden), debunked that in 2008, and in 2010 I gave the potential solutions in a paper in Pharmacoeconomics.

Can we move on please? From discussions I get the impression the EuroQoL Group understand this – plus they have funded a group of us to test one of my solutions. But there are other groups out there who aren’t up to speed.

For the Japanese group, I’ll just pose a question to a hypothetical scenario that, I hope, will make clear just why the “death state” thing is wrong.

Suppose you have a group of people who for whatever reason (perhaps religious) never pick “death” in preference to a health state.

QUESTION: What happens when you estimate a conditional logit model to get QALY weights?

If you counter with “there are always people who consider some states worse than death and then you can estimate the model, I’d suggest you go read Thurstone, Luce & Marley, and then the Louviere/Hensher stuff. A DCE is, technically, a model of THE INDIVIDUAL. You should, in principle, be able to estimate a model for an individual (if you give them enough choices – of course in practice we typically can’t but you should be able to in theory if your model really is a DCE i.e. rooted in random utility theory).

no capability not death

Just a quick note following a twitter exchange I had regarding whether capabilities as valued by the ICEPOP team (the ICECAP-O was referenced in the original paper) are “QALY-like”.

Key team members never intended the ICECAP-O scores to be multiplied by life expectancy (in the way, say, an EQ-5D score is). Whilst we have recognised that people would like to do this, technically this is a fudge and comes down to definitions and the maths:

Death necessarily implies no capabilities but no capabilities (the bottom ICECAP-O state) does not imply death. But more fundamentally, the estimated ICECAP scores are interval scaled, NOT ratio-scaled (for reference, read the BWS book): we used a linear transformation to preserve the relative differences between states but the anchoring at zero would not be accepted by a math psych person: they would say defining the bottom to be the zero doesn’t make it so.

Since different individuals technically had different zeros (the BWS or any discrete choice estimates have arbitrary zero) – death – multiplying a technically averaged interval scale score (our published tariff) by a ratio scaled one (life expectancy) to compare across groups/interventions is wrong: If there is heterogeneity in where “death” is on our latent capability scale (which we can’t/didn’t quantify – unlike the traditional QALY models estimated in the proper way) then comparisons across groups that don’t have the same “zero” gives incorrect answers. We can compare “mean losses of capability from full capability” which is why I personally (though I don’t speak for the wider team here) prefer the measure to be used as an alternative measure of deprivation, like the IMD in UK or SEIFA in Australia.