Scale characteristics and mapping accuracy of the US EQ-5D, UK EQ-5D, and SF-6D in patients with rheumatoid arthritis

Frederick Wolfe, Kaleb D Michaud, Gene Wallenstein

Research output: Contribution to journalArticle

15 Citations (Scopus)

Abstract

Objective. 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. Methods. 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. Results. 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. Conclusion. 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. The Journal of Rheumatology

Original languageEnglish (US)
Pages (from-to)1615-1625
Number of pages11
JournalJournal of Rheumatology
Volume37
Issue number8
DOIs
StatePublished - Aug 1 2010

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Rheumatoid Arthritis
Pain Measurement
Health
Rheumatology
Comorbidity
Linear Models
Demography
Pain
Surveys and Questionnaires

Keywords

  • Conversion
  • EQ-5D utilities
  • Health assessment questionnaire
  • SF-6D utilities

ASJC Scopus subject areas

  • Immunology and Allergy
  • Rheumatology
  • Immunology

Cite this

Scale characteristics and mapping accuracy of the US EQ-5D, UK EQ-5D, and SF-6D in patients with rheumatoid arthritis. / Wolfe, Frederick; Michaud, Kaleb D; Wallenstein, Gene.

In: Journal of Rheumatology, Vol. 37, No. 8, 01.08.2010, p. 1615-1625.

Research output: Contribution to journalArticle

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abstract = "Objective. 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. Methods. 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. Results. 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. Conclusion. 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. The Journal of Rheumatology",
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