This paper reports on the findings of a study to derive a preference-based algorithm for estimating a single index measure from the SF-36 for use in economic evaluation and population health surveys
The SF-36 was revised into a six dimensional health state classification called the SF-6D. A sample of 250 states defined by the SF-6D have been valued by a representative sample of 781 members of the UK general population, using standard gamble. The overall aim is to construct a model for predicting health state valuations for all 18,000 states defined by the SF-6D. The econometric modelling must cope with the hierarchical nature of the data (each individual values 6 health states) and its skewed distribution. The models have produced robust estimates of the 'main effects' and, in general, the results support the ordinality of the SF-6D scales. There are problems modelling interaction effects and we find little influence from personal characteristics. Random effects and mean models provide very similar results. The pain, mental health and physical functioning dimensions are the most important in determining the value assigned to a state. The models predict 70% of health state values to within |0.10| and 40% to within |0.05|. The choice of models for applying to SF-36 data and the implications for research will be discussed.
Learning Objectives: Participants should be able to 1) describe the methodology for obtaining preference information for the SF-36 and 2) recognise the application of the results in the conduct of economic evaluations
Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
I do not have any significant financial interest/arrangement or affiliation with any organization/institution whose products or services are being discussed in this session.