HERU is supported by the Chief Scientist Office of the Scottish Executive Health Department and the University of Aberdeen. The author accepts full responsibility for this talk. Health Economics Research Unit, University of Aberdeen Using DCEs to estimate utility weights within the framework of QALYs Professor Mandy Ryan

Structure What DCEs are and background to their use in Health Economics Application – developing a utility index in the area of glaucoma anchoring between 0 and 1 (John and Theresa) distinguishing ‘weight’ from ‘scale’ (Terry) assumption and analysis issues (Jorge, John + Theresa)

Discrete choice experiments Attribute based hypothetical survey measure of value Origins in mathematical psychology  Distinguish from conjoint analysis  Also known as ‘Stated preference discrete choice modelling’ Increasingly used in environmental, transport and health economics

Can’t have the best of everything! Legroom Food and drink Entertainment Reclining chair Ticket price Check-in service

Example of binary - Yes/No response Choice 1 Choice 3 Choice 4 Choice 5 Choice 6 Choice 2

Example of generic multiple choice – including a neither option

Discrete choice experiments Attribute based hypothetical survey measure of value Origins in mathematical psychology  Distinguish from conjoint analysis  Also known as ‘Stated preference discrete choice modelling’ Increasingly used in environmental, transport and health economics

DCEs – their use in HE Pre cost-benefit analysis  human capital approach  willingness to pay 1970s - cost-effectiveness analysis  e.g. cost per life year 1980s - cost-utility analysis  e.g. cost per Quality Adjusted Life Years (QALYs)  Standard gamble and time trade-offs 1990s - cost-benefit analysis  health, non-health and process attributes  Contingent valuation method and discrete choice experiments 2000 forward  the importance of factors beyond health outcomes  NICE WTP for a QALY Estimation of utility weights

HERU is supported by the Chief Scientist Office of the Scottish Executive Health Department and the University of Aberdeen. The author accepts full responsibility for this talk. Health Economics Research Unit, University of Aberdeen Eliciting a health state utility index using a discrete choice experiment: an application to Glaucoma Funded by Ross Foundation Jen Burr, Mary Kilonzo, Mandy Ryan, Luke Vale

Case Study - Glaucoma chronic eye disease - progressive damage to optic nerve does not reduce length of life but associated with impaired quality of life outcomes - intraocular pressure reduction and measures of visual function do not capture impact of condition or treatment on emotional and physical functioning or lifestyle Standard gamble and time trade-off not appropriate

Conducting a DCE Stage 1 - Identifying attributes and levels Stage 2 - Experimental design to determine choices Stage 3 - Collecting data  Principles of a good survey design Stage 4 - Data analysis  Discrete choice modelling Conditional logit model and developments –nested logit, random parameter logit

Attributes and Levels Attributes Central and Near Vision Lighting and glare Mobility Activities of daily living Local eye discomfort Other effects of glaucoma and treatment Levels No difficulty Some difficulty Quite a bit of difficulty Severe difficulty

Experimental design Fractional factorial design of 32 choices  Main effects no interactions Properties  Orthogonality  Level balance  Minimum overlap

Example of a DCE choice – respondents were asked what they think is WORSE SITUATION ASITUATION B No difficulty with:  Central and near vision  Lighting and glare  Mobility Some difficulty with:  Activities of daily living  Eye discomfort  Other effects of glaucoma and its treatment No difficulty with:  Central and near vision Some difficulty with:  Lighting and glare Quite a lot of difficulty with:  Activities of daily living  Other effects of glaucoma and its treatment Severe difficulty with:  Mobility  Eye discomfort (Tick one box only) Situation A Situation B

Rationality tests  Dominance tests too easy and may question credibility of experiment  Sen’s expansion and contraction rationality tests used

Data collection Subjects from 4 hospital-based clinics and 1 community-based glaucoma clinic across two eye centres in the UK (Aberdeen and Leeds) received questionnaire (n=225) Also recruited volunteers from the International Glaucoma Association (IGA) (n=248)

Analysis of DCE QW ij = ∑  dl X dl + e + u where  QW ij is the quality weight for outcome state i as valued by individual j  X dl is a vector of dummy variables where d represents the attribute from the profile measure l the level of that attribute

Estimating utility weights summation of the coefficients associated with the best level for each attribute Rescaled between zero (worse level of all attributes) and 1 (best level of all attributes)

Response rates and rationality 289 subjects responded to DCE questionnaire 3 respondents failed both consistency tests Analysis performed on 286 respondents Analysed according to severity

Results of the DCE Attributes and levelsCoefficient Central and near vision tasks No difficulty Some difficulty Quite a lot of difficulty Lighting and glare No, some and quite a lot of difficulty Mobility No difficulty Some difficulty Quite a lot of difficulty Visual judgement for activities of daily living No difficulty Some difficulty Quite a lot of difficulty Eye discomfort No difficulty Some and quite a lot of difficulty Other effects No difficulty Some and quite a lot of difficulty 0.169

Quality weights DimensionIndex Central and Near Vision No difficulty0.322 Some difficulty0.219 Quite a lot0.135 severe0 Lighting and glare No difficulty0.070 Some difficulty0 Quite a lot0 severe0 Mobility No difficulty0.237 Some difficulty0.148 Quite a lot0.090 severe0 DimensionIndex Activities of daily living No difficulty0.257 Some difficulty0.185 Quite a lot0.111 severe0 Eye discomfort No difficulty0.062 Some difficulty0.035 Quite a lot0.035 severe0 Other effects No difficulty0.052 Some difficulty0.043 Quite a lot0.043 severe0

Utility score for BEST health state Situation description Quality weights Utility Score You have no difficulty with central and near vision You have no difficulty with lighting and glare0.070 You have no difficulty with mobility0.237 You have no difficulty with activity of daily living0.257 You have no difficulty with local eye discomfort0.062 You have no difficulty with other effects of glaucoma and its treatments 0.052

Utility score for WORSE health state Situation description Quality weights Utility Score You have severe difficulty with central and near vision 00 You have severe difficulty with lighting and glare0 You have severe difficulty with mobility0 You have severe difficulty with activity of daily living0 You have severe difficulty with local eye discomfort0 You have severe difficulty with other effects of glaucoma and its treatments 0

Utility score for intermediate health state Situation description Quality weights Utility Score You have some difficulty with central and near vision You have some difficulty with lighting and glare0 You have some difficulty with mobility0.148 You have no difficulty with activity of daily living0.257 You have no difficulty with local eye discomfort0.062 You have no difficulty with other effects of glaucoma and its treatments 0.052

Some general points One of few studies to estimates utility weights from DCEs (though appears to be increasing) Programme specific! Response rate 62% good for DCE, though issues of generalisability are important Preferences differed according to severity

Points for Discussion Weights for use in programme specific QALY  What if want to generate generic QALY weights (anchored between DEATH and PERFECT HEALTH) How value DEATH? Distinguishing weight (importance of attribute) from scale (importance of attribute levels) Econometric analysis  Assumptions of logit model Errors terms independent, irrelevance of alternatives and heterogeneity  Decision making heuristics Do individuals trade across attributes