Mapping the Oxford hip score onto the EQ-5D utility index
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To assess different mapping methods for the estimation of a group’s mean EQ-5D score based on responses to the Oxford hip score (OHS) questionnaire.
Four models were considered: a) linear regression using total OHS as a continuous regressor; b) linear regression employing responses to the twelve OHS questions as categorical predictors; c) two-part approach combining logistic and linear regression; and d) response mapping. The models were internally validated on the estimation data set, which included OHS and EQ-5D scores for total hip replacements, both before and six months after procedure for 1,759 operations. An external validation was also performed.
All models estimated the mean EQ-5D score within 0.005 of an observed health-state utility estimate, ordinary least squares (OLS) continuous being the most accurate and OLS categorical the most consistent. Age, gender and deprivation did not improve the models. More accurate estimations at the individual level were achieved for higher scores of observed OHS and EQ-5D.
Based on these results, when EQ-5D scores are not available, answers to the OHS questionnaire can be used to estimate a group’s mean EQ-5D with a high degree of accuracy.
KeywordsEQ-5D Health utility Oxford hip score Hip replacement Outcomes mapping
Oxford hip score
Total hip replacement
Ordinary least squares
Health-related quality of life
Transfer to utility regression
Mean absolute error
This study was funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research funding scheme. The views expressed in this paper are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. Support was also received from the NIHR Biomedical Research Unit into Musculoskeletal Disease, Nuffield Orthopaedic Centre and University of Oxford. We would like to acknowledge Dr. Richard E. Field for kindly allowing us to use the data collected at the South West London Elective Orthopaedics Centre under his supervision and for reviewing the final draft. We are grateful to the COASt project group at the Nuffield Department of Orthopaedics, Rheumatology and Muskuloskeletal Sciences at the University of Oxford and especially Dr. Kassim Javaid and Dr. Amit Kiran for the helpful discussions on earlier versions of this paper. We would also like to thank Dr. Oliver Rivero-Arias from the Health Economics Research Centre at the University of Oxford for clarifications on the response mapping approach. An earlier version of this work was presented at the winter 2011 conference of the Health Economists’ Study Group at the University of York, and we are grateful for the comments received there.
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