Estimating utilities from
individual preference data
Some introductory remarks by
Tony O’Hagan
Welcome!
• Welcome to the sixth CHEBS dissemination
workshop
• This forms part of our Focus Fortnight on
“Estimating utilities from individual preference
data”
• Our format allows plenty of time for discussion
of the issues raised in each talk, so please feel
free to join in!
Health state
• Health state is a many-faceted thing
• Several descriptive systems exist
› EQ5D, SF6D, HUI
› disease-specific descriptors
• The typical scheme assigns a (discrete) score on
each of a number of health dimensions
› Scores on each dimension are generally more or less
loosely defined
Quality of Life
• We seek a function that maps the multidimensional health state to a single number
› The value to be assigned to a health state is a
measure of health-related quality of life (HRQoL)
› Perfect health = 1
› Immediate death = 0
› Possibility of states worse than death
• This represents the principal utility measure in
health economics
Utility
• In cost-effectiveness analysis of health
technologies, the “gold standard” measure of
benefit to patients is the QALY
• QALYs are utility multiplied by time
› One QALY equates to one year of perfect health
• Cost-effectiveness analysis using QALYs is often
called cost-utility analysis
• We won’t discuss here the shortcomings of
HRQoL measures and QALYs!
Preference data
• Data obtained from individuals
• Each person values one or more health states
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Time trade off (TTO)
Standard gamble (SG)
Visual analogue scale (VAS)
Rankings
• TTO, SG and VAS provide numeric values that
should be on the utility scale
› In practice, this is questionable!
Modelling
• Statistical modelling is needed to link patient
preference data to the underlying utility
• Several important issues arise
› Individuals make errors of judgement in comparing
health states – not necessarily coherent
› Errors can’t be symmetric or homoscedastic
» Because of the upper limit of 1
› Individuals respond to poor health differently
» Especially in regard to states worse than death
» Individual-level covariates may be available
Whose utility is it anyway?
• Variation between individuals raises a more
fundamental question
• Each person has their own utility function
• We want a kind of societal utility function
• The relationship between the two is ill-defined
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Societal = mean or median individual?
Additivity can’t be preserved if we use medians
But skewness at the individual level is marked
Society should be able to over-rule individuals (e.g.
capital punishment)
Functional form
• The underlying functional relationship between
utility and health state could be almost anything
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Should decrease in each dimension
Various regression models have been fitted
Additivity between dimensions is questionable
Nonparametric model
• Variation between countries
Using utilities
• Utilities are required in economic models
› Need to be able to assign utility to health states
arising in model
› Need to quantify uncertainty for PSA
› Correlation is important (otherwise realisations can
be implausible)
• Also in analysis of cost-effectiveness trials
› Missing data
› Need to translate between descriptive systems
› Same issues of quantifying uncertainty