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

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
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
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
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.
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
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
Distribution of chained health state valuations
400
300
200
100
0
-0.75 -0.50 -0.25
0.00
0.25
0.50
0.75
1.00
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