Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score
PURPOSE: The Oxford Knee Score (OKS) is a validated 12-item measure of knee replacement outcomes. An algorithm to estimate EQ-5D utilities from OKS would facilitate cost-utility analysis on studies analyses using OKS but not generic health state preference measures. We estimate mapping (or cross-wal...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Springer Verlag
2013
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| _version_ | 1797072880775725056 |
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| author | Dakin, H Gray, A Murray, D |
| author_facet | Dakin, H Gray, A Murray, D |
| author_sort | Dakin, H |
| collection | OXFORD |
| description | PURPOSE: The Oxford Knee Score (OKS) is a validated 12-item measure of knee replacement outcomes. An algorithm to estimate EQ-5D utilities from OKS would facilitate cost-utility analysis on studies analyses using OKS but not generic health state preference measures. We estimate mapping (or cross-walking) models that predict EQ-5D utilities and/or responses based on OKS. We also compare different model specifications and assess whether different datasets yield different mapping algorithms. METHODS: Models were estimated using data from the Knee Arthroplasty Trial and the UK Patient Reported Outcome Measures dataset, giving a combined estimation dataset of 134,269 questionnaires from 81,213 knee replacement patients and an internal validation dataset of 45,213 questionnaires from 27,397 patients. The best model was externally validated on registry data (10,002 observations from 4,505 patients) from the South West London Elective Orthopaedic Centre. Eight models of the relationship between OKS and EQ-5D were evaluated, including ordinary least squares, generalized linear models, two-part models, three-part models and response mapping. RESULTS: A multinomial response mapping model using OKS responses to predict EQ-5D response levels had best prediction accuracy, with two-part and three-part models also performing well. In the external validation sample, this model had a mean squared error of 0.033 and a mean absolute error of 0.129. Relative model performance, coefficients and predictions differed slightly but significantly between the two estimation datasets. CONCLUSIONS: The resulting response mapping algorithm can be used to predict EQ-5D utilities and responses from OKS responses. Response mapping appears to perform particularly well in large datasets. |
| first_indexed | 2024-03-06T23:14:03Z |
| format | Journal article |
| id | oxford-uuid:667e3772-8cf6-481e-9e3e-8fb4d0f6ecef |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-06T23:14:03Z |
| publishDate | 2013 |
| publisher | Springer Verlag |
| record_format | dspace |
| spelling | oxford-uuid:667e3772-8cf6-481e-9e3e-8fb4d0f6ecef2022-03-26T18:32:15ZMapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee ScoreJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:667e3772-8cf6-481e-9e3e-8fb4d0f6ecefEnglishSymplectic Elements at OxfordSpringer Verlag2013Dakin, HGray, AMurray, DPURPOSE: The Oxford Knee Score (OKS) is a validated 12-item measure of knee replacement outcomes. An algorithm to estimate EQ-5D utilities from OKS would facilitate cost-utility analysis on studies analyses using OKS but not generic health state preference measures. We estimate mapping (or cross-walking) models that predict EQ-5D utilities and/or responses based on OKS. We also compare different model specifications and assess whether different datasets yield different mapping algorithms. METHODS: Models were estimated using data from the Knee Arthroplasty Trial and the UK Patient Reported Outcome Measures dataset, giving a combined estimation dataset of 134,269 questionnaires from 81,213 knee replacement patients and an internal validation dataset of 45,213 questionnaires from 27,397 patients. The best model was externally validated on registry data (10,002 observations from 4,505 patients) from the South West London Elective Orthopaedic Centre. Eight models of the relationship between OKS and EQ-5D were evaluated, including ordinary least squares, generalized linear models, two-part models, three-part models and response mapping. RESULTS: A multinomial response mapping model using OKS responses to predict EQ-5D response levels had best prediction accuracy, with two-part and three-part models also performing well. In the external validation sample, this model had a mean squared error of 0.033 and a mean absolute error of 0.129. Relative model performance, coefficients and predictions differed slightly but significantly between the two estimation datasets. CONCLUSIONS: The resulting response mapping algorithm can be used to predict EQ-5D utilities and responses from OKS responses. Response mapping appears to perform particularly well in large datasets. |
| spellingShingle | Dakin, H Gray, A Murray, D Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score |
| title | Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score |
| title_full | Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score |
| title_fullStr | Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score |
| title_full_unstemmed | Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score |
| title_short | Mapping analyses to estimate EQ-5D utilities and responses based on Oxford Knee Score |
| title_sort | mapping analyses to estimate eq 5d utilities and responses based on oxford knee score |
| work_keys_str_mv | AT dakinh mappinganalysestoestimateeq5dutilitiesandresponsesbasedonoxfordkneescore AT graya mappinganalysestoestimateeq5dutilitiesandresponsesbasedonoxfordkneescore AT murrayd mappinganalysestoestimateeq5dutilitiesandresponsesbasedonoxfordkneescore |