To compare the US EQ-5D with the UK EQ-5D and the SF-6D in patients with rheumatoid arthritis (RA). To provide mappings for each of the scales based on clinical variables.
We studied 12,424 patients with RA with 66,958 longitudinal observations using linear regression. In our mapping models we used the Health Assessment Questionnaire (HAQ) as a continuous predictor variable and as individual items. More complex models included the addition of a visual analog pain scale, the mood scale from the SF-36, and demographic and comorbidity covariates. We compared various models using root mean squared error (RMSE), in-sample and out-of-sample mean absolute error (MAE), and other measures of prediction accuracy and model fit.
At any level of clinical severity, the US EQ-5D always had a higher utility score than the UK EQ-5D; and overall, the US scores were 0.094 units higher. The best models explained 64% to 72% of variance in utility scores, with RMSE values of 0.07 (SF-6D), 0.11 (EQ-5D US), and 0.17 (UK EQ-5D). There was a substantial increase in predictive accuracy by using pain and mood as predictor variables in the mapping.
The US EQ-5D differs from the UK version and from the SF-6D in mean scores and ranges. When determined by mapping, the US EQ-5D has a much lower prediction error than the UK EQ-5D. Simple mapping models that use HAQ and pain have acceptable error rates, although more complex models that include mood scores and individual HAQ items substantially improve predictive accuracy.