• Sean D Rundell, Brian W Bresnahan, Patrick J Heagerty, Bryan A Comstock, Janna L Friedly, Jeffrey G Jarvik, and Sean D Sullivan.
• Comparative Effectiveness, Cost, and Outcomes Research Center, University of Washington, Seattle (SDR, BWB, JLF, JGJ)Department of Radiology, University of Washington, Seattle (SDR, BWB, JGJ)Pharmaceutical Outcomes Research & Policy Program, University of Washington, Seattle (BWB, SDS)Center for Biomedical Statistics, University of Washington, Seattle (PJH, BAC)Department of Rehabilitation Medicine, University of Washington, Seattle (JLF)Departments of Neurological Surgery, Health Services, Pharmacy and Orthopedics & Sports Medicine, University of Washington, Seattle (JGJ) srundell@uw.edu.
• Med Decis Making. 2014 Oct 1;34(7):873-83.

BackgroundLinking patient-reported back pain outcomes with health utility measures is valuable for informing economic evaluations.MethodsWe used the Back pain Outcomes using Longitudinal Data (BOLD) registry to assess back pain and quality-of-life measures. The BOLD registry includes participants ≥65 years from 3 health systems. We used multiple baseline outcome measures: Roland-Morris Disability Questionnaire (RMDQ), Euroqol-5D (EQ-5D), and back and leg pain numerical rating scales (NRS). To develop and validate a model, we used a standard split-sample method and a novel multisite validation approach. We applied linear regression to map RMDQ to EQ-5D, adjusting for age, sex, pain numerical rating scores, and nonlinear transformations of outcome measures. We computed R (2), root mean squared error, and mean absolute error (MAE) for purposes of model selection. The final model included EQ-5D as the dependent variable with independent variables of age, RMDQ, and back NRS. We used this model to predict EQ-5D scores in validation samples.ResultsIn total, 5224 participants had both baseline RMDQ and EQ-5D. Mean age was 74 years (65% female). Negative correlations (-0.72) were observed at baseline for RMDQ and EQ-5D. The selected model from all developmental samples had R (2) >0.41 and MAE < 0.119. Validation analyses indicated no differences in estimated v. observed mean EQ-5D scores in the split sample. Validation using the multisite validation approach identified prediction error variability, MAE of 0.081 to 0.119, when predicting EQ-5D.LimitationsThe statistical relationship may not generalize well to all study populations as we demonstrated in our multisite analysis.ConclusionsAn empirical algorithm predicting EQ-5D weights from RMDQ scores provides a currently unavailable link for conducting economic evaluations in low back pain studies.© The Author(s) 2014.

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