I may be misinterpreting something, but I think what Emrik has described is basically how generic multi-attribute utility instruments (MAUIs) are used by health economists in the calculation of QALYs.
For example, as described in this excellent overview, the EQ-5D questionnaire asks about 5 different dimensions of health, which are then valued in combination:
mobility (ability to walk about)
self-care (ability to wash and dress yourself)
usual activities (ability to work, study, do housework, engage in leisure activities, etc.)
pain/discomfort
anxiety/depression
Each level is scored 1 (no problems), 2 (moderate problems), or 3 (extreme problems). These scores are combined into a five-digit health state profile, e.g., 21232 means some problems walking about, no problems with self-care, some problems performing usual activities, extreme pain or discomfort, and moderate anxiety or depression. However, this number has no mathematical properties: 31111 is not necessarily better than 11112, as problems in one dimension may have a greater impact on quality of life than problems in another. Obtaining the weights for each health state, then, requires a valuation exercise.
Valuation methods
There are many ways of generating a value set (set of weights or utilities) for the health states described by a health utility instrument. (For reviews, see e.g., Brazier, Ratcliffe, et al., 2017 or Green, Brazier, & Deverill, 2000; they are also discussed further in Part 2.) The following five are the most common:
Time tradeoff: Respondents directly trade off duration and quality of life, by stating how much time in perfect health is equivalent to a fixed period in the target health state. For example, if they are indifferent between living 10 years with moderate pain or 8 years in perfect health, the weight for moderate pain (state 11121 in the EQ-5D-3L) is 0.8.
Standard gamble: Respondents trade off quality of life and risk of death, by choosing between a fixed period (e.g., 10 years) in the target health state and a “gamble” with two possible outcomes: the same period in perfect health, or immediate death. If they would be indifferent between the options when the gamble has a 20% probability of death, the weight is 0.8.
Discrete choice experiments: Respondents choose the “best” health state out of two (or sometimes three) options. Drawing on random utility theory, the location of the utilities on an interval scale is determined by the frequency each is chosen, e.g., if 55% of respondents say the first person is healthier than the second (and 45% the reverse), they are close together, whereas if the split is 80:20 they are far apart. This ordinal data then has to be anchored to 0 and 1; some ways of doing so are presented in Part 2. Less common ordinal methods include:
Ranking: Placing several health states in order of preference.
Best-worst scaling: Choosing the best and worst out of a selection of options.
Visual analog scale: Respondents mark the point on a thermometer-like scale, usually running from 0 (e.g., “the worst health you can imagine”) to 100 (e.g., “the best health you can imagine”), that they feel best represents the target health state. If they are also asked to place “dead” on the scale, a QALY value can be easily calculated. For example, with a score of 90⁄100 and a dead point of 20⁄100, the weight is (90-20)/(100-20) = 70⁄80 = 0.875.
Person tradeoff (previously called equivalence studies): Respondents trade off health (and/or life) across populations. For example, if they think an intervention that moves 500 people from the target state to perfect health for one year is as valuable as extending the life of 100 perfectly healthy people for a year, the QALY weight is 1 – (100/500) = 0.8.[13]
Thanks. I have seen similar valuation methods elsewhere which might interest you. 1000minds’ Multi-Criteria Decision Analysis (MCDA/MCDM) article has a list of methods, with summaries, like Direct rating, Points allocation, SMART, SMARTER, AHP, etc.
So, when you have 31111, each number is in a separate dimension and there’s no problem so far. Then the valuation method handles the hard part. Each valuation method you quote (and many MCDM ones) have a common property: they rely on the intuition or judgment of a decision maker. The decision maker is asked to make comparisons involving multiple dimensions. But that doesn’t explain how to do it; it relies on people somehow doing it by unspecified methods and then stating answers. Does that make sense and do you see the problem? Do you think you know how decision makers can come up with the answers needed in by these valuation methods?
Put another way, I read the valuation methods as attempts to make pre-existing knowledge more explicit and quantified. It assumes a decision maker already knows some answers about how to value different dimensions against each other, rather than telling him how to do it. But I’m interested in how to get the knowledge in the first place.
I may be misinterpreting something, but I think what Emrik has described is basically how generic multi-attribute utility instruments (MAUIs) are used by health economists in the calculation of QALYs.
For example, as described in this excellent overview, the EQ-5D questionnaire asks about 5 different dimensions of health, which are then valued in combination:
Thanks. I have seen similar valuation methods elsewhere which might interest you. 1000minds’ Multi-Criteria Decision Analysis (MCDA/MCDM) article has a list of methods, with summaries, like Direct rating, Points allocation, SMART, SMARTER, AHP, etc.
So, when you have 31111, each number is in a separate dimension and there’s no problem so far. Then the valuation method handles the hard part. Each valuation method you quote (and many MCDM ones) have a common property: they rely on the intuition or judgment of a decision maker. The decision maker is asked to make comparisons involving multiple dimensions. But that doesn’t explain how to do it; it relies on people somehow doing it by unspecified methods and then stating answers. Does that make sense and do you see the problem? Do you think you know how decision makers can come up with the answers needed in by these valuation methods?
Put another way, I read the valuation methods as attempts to make pre-existing knowledge more explicit and quantified. It assumes a decision maker already knows some answers about how to value different dimensions against each other, rather than telling him how to do it. But I’m interested in how to get the knowledge in the first place.