Estimating utilities from individual preference data

1. Estimating utilities from individual preference data Some introductory remarks by Tony O’Hagan

2. 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!

3. 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

4. Quality of Life • We seek a function that maps the multi-dimensional 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

5. 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!

6. Preference data • Data obtained from individuals • Each person values one or more health states • 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!

7. 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

8. 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 • 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)

9. Functional form • The underlying functional relationship between utility and health state could be almost anything • Should decrease in each dimension • Various regression models have been fitted • Additivity between dimensions is questionable • Nonparametric model • Variation between countries

10. 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