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    Nancy Devlin Nancy Devlin Presentation Transcript

    • Improved methods for valuing EQ-5D-5L Professor Nancy Devlin ndevlin@ohe.org Director of Research Office of Health Economics, London Spanish Health Economics Association June 24, 2010
    • Content • Why are new methods required? – EQ-5D-5L – Problems with conventional methods – Opportunities presented by computer-aided methods • LT-TTO • Results from ongoing research • DCE + VAS • Preparing for national EQ-5D-5L value sets Spanish Health Economics Association June 24, 2010
    • Why develop new methods? (i) EQ-5D-5L • ‘Cross-over studies’ will provide interim values, but ultimately a value set based on preferences directly elicited for EQ-5D-5L states is required. Challenges in valuing EQ-5D-5L: 5L 100% 3L 5L 3L 5L 3L 3L 5L 3L 5L • More states to be valued (55 = 3125, compared with 35 = 243) 80% • How best to elicit preferences for sufficient states across % problem 60% descriptive system, to provide an adequate basis for the modelling? 40% • Greater ‘subtlety’ between levels/labels: potential challenge 20% methods where states are considered ‘one by one’ for • A ‘mixed methods’ approach may have more merit than reliance 0% on TTO alone Mobility discomfort Selfcare activities depression Anxiety / Usual Pain / Spanish Health Economics Association June 24, 2010
    • Why develop new methods? (ii) Addressing known problems • The MVH protocol has come to be the de facto EQ-5D valuation protocol. • Yet is subject to known, non-trivial problems • Substantial issues remain – principal among which is the valuation of states worse than dead (< 0). – The ‘standard’ TTO protocol cannot elicit values < 0 – A different method is required - meaning values > 0 and < 0 are non-comparable – the method for values < 0 yields extreme values, requiring post hoc transformation (eg. to -1) Spanish Health Economics Association June 24, 2010
    • Conventional TTO Conventional TTO for state better than dead U(H) > 0 full health Life A Hi Life B duration in Hi = 10 years Conventional TTO for states worse than dead U (H) <0 Hi full health Life A Immediate death Life B Spanish Health Economics Association June 24, 2010
    • Why develop new methods? (iii) Exploiting new technologies • Digital aids have largely replaced physical ‘props’ and ‘prompts’ to valuation tasks. • Digital aids do not just replicate physical props, but offer greater functionality, e.g. – Built in randomisation procedures re: states and tasks – Automated iterative procedures (greater consistency between interviewers; less human error in prompting and recording participant responses) – Time stamping all responses – Central data capture and storage; eliminating data entry Spanish Health Economics Association June 24, 2010
    • Lead Time TTO (LT-TTO) ‘Lead time’ TTO (state happens to be better than dead) lead time full health Life A lead time Hi Life B duration in Hi = 10 years ‘Lead time’ TTO (state happens to be worse than dead) lead time Life A lead time Hi Life B duration in Hi = 10 years • Approach described by Robinson and Spencer (2006) Health Economics. • LT-TTO shown to be feasible - Devlin et al (2010) Health Economics. • A ‘lag time’ equivalent is also possible: order of states in Life B reversed – Tilling et al (2010) Medical Decision Making. Spanish Health Economics Association June 24, 2010
    • Refining/testing the LT-TTO: current research • 1, 5, 10 year durations; lead: duration: 2:1, 5:1 • n = 208 participants, blocked into groups defined by pairs of variants/ordering and one of two sets of states. • Each participant valued 5 EQ-5D states using two variants i.e. 10 TTO tasks • Data collection: May/June 2010. [a] [b] [c] [d] Group Variant Duration (years): 10 1 5 5 pairs Lead time (years): 20 5 10 1 (a) + (b) Lag (years) 10 2 (a) + (c) Ratio* of 2:1 5:1 2:1 2:1 3 (b) + (c) lead(lag):duration 4 (c) + (d) Spanish Health Economics Association June 24, 2010
    • Initial results (i): values by ‘category’, by variant By variant Categories: 1. Full health 2. Better than dead 1. U = 1 (‘non-trading’) 3. Equivalent to dead a 4. Valued using lead time 5. valuation by extension and/or reduction 2. U > 0 and < 1 6. Refuses any trade involving health state 3. U = 0 9. missing 1. Full health 2. Better than dead 4. U < 0 3. Equivalent to dead ( & valued within the b 4. Valued using lead time 5. valuation by extension and/or reduction 6. Refuses any trade involving health state available lead/lag 9. missing 1. Full health time) 2. Better than dead 5. U < 0 3. Equivalent to dead c 4. Valued using lead time 5. valuation by extension and/or reduction (& valued using 6. Refuses any trade involving health state 9. missing extended led/lag 1. Full health 2. Better than dead procedure) 3. Equivalent to dead d 4. Valued using lead time 5. valuation by extension and/or reduction 6. ‘Refuses trade’ 6. Refuses any trade involving health state 7. Missing 9. missing 0 100 200 300 400 500 Count Nb: x-axis shows frequency, not % Spanish Health Economics Association June 24, 2010
    • Initial results (ii): distribution of values (all variants); and exhausting lead time, state 33333 80 Variant % who exhaust 60 lead time in valuing 33333 Frequency 40 a 0.21 b 0.20 20 c 0.18 d 0.28 0 -6 -4 -2 0 2 valuation Spanish Health Economics Association June 24, 2010
    • Initial results (iii): mean values by state by variant Mean values, including ‘extended lead’ values, but excluding ‘missing’ values LT-TTO variants EQ-5D state a b c d 11112 0.77 0.57 0.77 0.81 11122 0.67 0.36 0.61 0.56 11211 0.87 0.63 0.80 0.80 12111 0.79 0.71 0.77 0.76 22121 0.52 0.47 0.63 0.68 23232 -0.41 -1.35 -0.54 -0.26 33333 -1.0 -3.92 -1.18 -1.10 Spanish Health Economics Association June 24, 2010
    • Initial results (iv): distribution of values > 0 by variant a b 15 10 5 0 Density c d 15 10 5 0 0 .5 1 0 .5 1 valuation Graphs by Variant Spanish Health Economics Association June 24, 2010
    • Discrete Choice Modeling • DCEs widely used in health services research in the UK • The methods are grounded in theory eg. Thurstone (1927), through to McFadden (1974, 1989). • Based on the idea of choices reflecting trade-offs and underlying preferences (NICE considers this important in its choice of valuations – NICE 2008) • Participants are asked to choose from pairs of scenarios (EQ-5D profiles): simple to complete. • Potential advantages in valuing EQ-5D-5L: feasible for self-completion; each participant can complete many DCE valuation tasks. • But problems ‘anchoring’ values at 0 and 1 • DCE to generate preference data to supplement TTO data • Strength of preference data may assist: DCE accompanied by VAS • Alternative ways of modelling these data will be explored. Spanish Health Economics Association June 24, 2010
    • Experimentation: DCE +VAS Compare health states A and B. Imagine for each health state that you are in that state yourself. A B Confined to bed No problems in walking about Some problems washing or dressing myself Unable to wash or dress myself Some problems with performing my usual activities Unable to perform my usual activities No pain or discomfort Extreme pain or discomfort Not anxious or depressed Extremely anxious or depressed Which health state is best in your opinion, A or B? Tick the box A B Spanish Health Economics Association June 24, 2010
    • The best health you can imagine VAS Task 100 95 90 A 85 Confined to bed 80 75 Some problems washing or dressing myself 70 Some problems with performing my usual 65 activities 60 No pain or discomfort 55 Not anxious or depressed 50 45 B 40 No problems in walking about 35 30 Unable to wash or dress myself 25 Unable to perform my usual activities 20 Extreme pain or discomfort 15 Extremely anxious or depressed 10 5 Spanish Health Economics Association 0 June 24, 2010 The worst health you can imagine
    • Preparing for EQ-5D-5L value sets – Issues with methodology remain – Some of which we hope to work out prior to EQ-5D-5L value set studies – 4-country study: 2010/2011 – EQ-5D-5L value sets: 2011/12 Spanish Health Economics Association June 24, 2010