Abstract
Background
Mapping Patient-Reported Outcomes Measurement Information System–Global Health (PROMIS-GH) to EuroQol 5-dimension, three-level version (EQ-5D-3L) provides a utility score for use in quality-of-life and cost-effectiveness analyses. In 2009, Revicki et al. mapped the PROMIS-GH items to EQ-5D-3L utilities using linear regression (REVReg). More recently, regression was shown to be ill-suited for mapping to preference-based measures due to regression to the mean. Linear and equipercentile equating are alternative mapping methods that avoid the issue of regression to the mean. Another limitation of the prior models is that ordinal predictors were treated as continuous.
Methods
Using data collected from the PROMIS Wave 1 sample, we refit REVReg, treating the PROMIS-GH items as categorical variables (CATReg). We applied linear and equipercentile equating to the REVReg model (REVLE, REVequip) and the CATReg model (CATLE, CATequip). We validated and compared the predictive accuracy of these models in a large sample of neurological patients at a single tertiary-care hospital.
Results
In the neurological disease patient sample, CATLE produced the strongest correlations between estimated and observed EQ-5D-3L scores and had the lowest mean squared error. The CATequip model had the lowest mean absolute error and had estimated scores that best matched the overall distribution of observed scores.
Conclusions
Using linear and equipercentile equating, we created new models mapping PROMIS-GH items to EQ-5D-3L utility scores. EQ-5D-3L utility scores can be more accurately estimated using our models for use in cost-effectiveness studies or studies examining overall health-related quality of life.
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Acknowledgements
The authors thank the Neurological Institute Center for Outcomes Research and Evaluation for supporting this study.
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Conceived and designed the study: NRT, ILK. Analyzed the data: NRT. Wrote first draft: NRT. Critically revised the manuscript: All authors. Reviewed and approved the final manuscript: All authors.
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No external funding was received.
Conflict of interest
Nicolas Thompson has received salary support from Novartis Pharmaceuticals for research outside the submitted work. Brittany Lapin and Irene Katzan declare that they have no conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. For this type of study formal consent is not required.
Informed consent
The study was approved by the Cleveland Clinic Institutional Review Board. Because the study consisted of analyses of pre-existing data, the requirement for patient informed consent was waived.
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40273_2017_541_MOESM1_ESM.docx
e-Fig 1 Empirical cumulative distribution functions for each of the REVReg, REVLE, REVequip, CATReg, CATLE, and CATequip models in the PROMIS Wave 1 (N = 13,955) and Cleveland Clinic neurological disease (N = 6906) samples. CCND Cleveland Clinic Neurological Disease sample, EQ-5D-3L EuroQol 5-Dimensions, three-level version, REV Reg Revicki’s original linear regression model 3, REV LE Revicki’s original linear regression model 3 with linear equating applied to the predicted values, REV equip Revicki’s original linear regression model 3 with equipercentile equating applied to the predicted values, CAT Reg linear regression model where PROMIS Global Health items are treated as categorical predictors, CAT LE model that applies linear equating to the predicted values of a linear regression model where PROMIS Global Health items are treated as categorical predictors, CAT equip model that applies equipercentile equating to the predicted values of a linear regression model where PROMIS Global Health items are treated as categorical predictors, PW1 PROMIS Wave 1 Sample (DOCX 70 kb)
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Thompson, N.R., Lapin, B.R. & Katzan, I.L. Mapping PROMIS Global Health Items to EuroQol (EQ-5D) Utility Scores Using Linear and Equipercentile Equating. PharmacoEconomics 35, 1167–1176 (2017). https://doi.org/10.1007/s40273-017-0541-1
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DOI: https://doi.org/10.1007/s40273-017-0541-1