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Samer A Kharroubi, Tony O’Hagan, John Brazier,

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,. Short-form 36 health survey questionnaire. a 36 item questionnaire for self-completion

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Samer A Kharroubi, Tony O’Hagan, John Brazier,

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  1. Valuing the SF-6D: a nonparametric approach using individual level preference dataPart 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 • measures general health across 8 dimensions • most widely used measure of general health • translated into over 20 languages • 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 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 role limitation social functioning pain mental health vitality • Each dimension has multiple levels of severity • Health state defined by selecting one level from each dimension • SF-6D defines 18,000 health states • 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 • 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 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.

  6. Results of valuation survey • Response rate: 65% (836/1445). • Representative in terms of age, education and social class • 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 • 3518 valuations across 249 health states

  7. Standard Gamble valuations • Average number of valuations per state was 14 • mean health state valuations ranged between 0.21 to 0.99 • standard deviations were often large (around 0.2 to 0.4) • 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 • 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 • But, some inconsistencies with SF-6D remain • But, tendency remains to under predict at the lower end ⇒ Look at alternative methods of estimation

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