Using DCEs to estimate utility weights within the framework of QALYs Professor Mandy Ryan

1. Using DCEs to estimate utility weights within the framework of QALYs Professor Mandy Ryan

2. 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)

3. Discrete choice experiments • Attribute based hypotheticalsurvey 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

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

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

6. Example of generic multiple choice – including a neither option

7. Discrete choice experiments • Attribute based hypotheticalsurvey 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

8. DCEs – their use in HE • Pre 1970 - 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

9. Eliciting a health state utility index using a discrete choice experiment: an application to GlaucomaFunded by Ross Foundation Jen Burr, Mary Kilonzo, Mandy Ryan, Luke Vale

10. 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

11. 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

12. 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 Attributes and Levels

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

14. Example of a DCE choice – respondents were asked what they think is WORSE

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

16. 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)

17. Analysis of DCE • QWij = ∑dlXdl + e + u • where • QWij is the quality weight for outcome state i as valued by individual j • Xdl is a vector of dummy variables • where d represents the attribute from the profile measure • l the level of that attribute

18. 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)

19. 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

20. Results of the DCE

21. Quality weights

22. Utility score for BEST health state

23. Utility score for WORSE health state

24. Utility score for intermediate health state

25. 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

26. 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