1. Valuing the SF-6D: a nonparametric approach using individual level preference data Part 1): The SF-6D and its valuation Samer A Kharroubi, Tony O’Hagan, John Brazier,

2. Short-form 36 health survey questionnaire a 36 item questionnaire for self-completion a 36 item questionnaire for self-completion measures general health across 8 dimensions measures general health across 8 dimensions most widely used measure of general health most widely used measure of general health translated into over 20 languages translated into over 20 languages validity tested across a wide range of conditions validity tested across a wide range of conditions

3. Stages of research 1) to adapt the SF-36 into a simplified health state classification amenable to valuation amenable to valuation 2) value a sample of states defined by the new classification 3) use multivariate statistical analysis to estimate an algorithm for scoring the new classification

4. Stage 1: The new health state classification: SF-6D 6 dimensions: physical functioning 6 dimensions: physical functioning role limitation role limitation social functioning pain mental health vitality vitality Each dimension has multiple levels of severity Each dimension has multiple levels of severity Health state defined by selecting one level from each dimension Health state defined by selecting one level from each dimension SF-6D defines 18,000 health states SF-6D defines 18,000 health states All existing SF-36 data can be assigned to the new classification All existing SF-36 data can be assigned to the new classification

5. Valuing health states defined by the SF-6D 249 health states (out of a possible 18,000) defined by SF-6D selected for the valuation survey 249 health states (out of a possible 18,000) defined by SF-6D selected for the valuation survey representative sample of 836 members of the UK general population seen by trained interviewers representative sample of 836 members of the UK general population seen by trained interviewers respondents asked to rank and value SF-6D health states by the ‘ping pong’ version of standard gamble respondents asked to rank and value SF-6D health states by the ‘ping pong’ version of standard gamble Respondents valued 5 states against full health (state 111111) and ‘PITS’ (645655) Respondents valued 5 states against full health (state 111111) and ‘PITS’ (645655) the pits state was valued against full health and death and non-pits states chained onto full-death scale. the pits state was valued against full health and death and non-pits states chained onto full-death scale.

6. Results of valuation survey Response rate: 65% (836/1445). Response rate: 65% (836/1445). Representative in terms of age, education and social class Representative in terms of age, education and social class Exclusions: Exclusions: –for not valuing ‘pits’ state = 130 (15.6%) –for valuing less than two health states = 9 (1.1%) –for giving the same valuation of all states = 86 (10.3%) –more likely to be older, male and have manual occupation Number of respondents for analysis 611 Number of respondents for analysis 611 –3518 valuations across 249 health states

7. Standard Gamble valuations Average number of valuations per state was 14 Average number of valuations per state was 14 mean health state valuations ranged between 0.21 to 0.99 mean health state valuations ranged between 0.21 to 0.99 standard deviations were often large (around 0.2 to 0.4) standard deviations were often large (around 0.2 to 0.4) distribution of values was negatively skewed distribution of values was negatively skewed

8. Distribution of chained health state valuations

9. Summary Basic models were OLS on mean health state values and RE on individual level data – these gave similar results Basic models were OLS on mean health state values and RE on individual level data – these gave similar results robust estimate of ‘main effects’ robust estimate of ‘main effects’ predictions: 79% correct to within +/-.1 and - 53% correct to within +/- 0.05; explanatory power of 0.51 for mean models; Mean absolute error around 0.07 predictions: 79% correct to within +/-.1 and - 53% correct to within +/- 0.05; explanatory power of 0.51 for mean models; Mean absolute error around 0.07 But, some inconsistencies with SF-6D remain But, some inconsistencies with SF-6D remain But, tendency remains to under predict at the lower end But, tendency remains to under predict at the lower end ⇒ Look at alternative methods of estimation