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

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Presentation transcript:

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 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

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 ›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 ›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 ›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