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Mapping SF-36 onto the EQ-5D index: how reliable is the relationship?

Rowen D, Brazier J, Roberts J - Health Qual Life Outcomes (2009)

Bottom Line: This paper compares the results to those obtained from existing mapping functions.The model including SF-36 dimensions, squared and interaction terms estimated using random effects GLS has the most accurate predictions of all models estimated here and existing mapping functions as indicated by MAE (0.127) and MSE (0.030).Our results suggest that models mapping the SF-36 onto the EQ-5D have similar predictions across inpatient and outpatient setting and medical conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Health Economics and Decision Science, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK. d.rowen@sheffield.ac.uk

ABSTRACT

Background: Mapping from health status measures onto generic preference-based measures is becoming a common solution when health state utility values are not directly available for economic evaluation. However the accuracy and reliability of the models employed is largely untested, and there is little evidence of their suitability in patient datasets. This paper examines whether mapping approaches are reliable and accurate in terms of their predictions for a large and varied UK patient dataset.

Methods: SF-36 dimension scores are mapped onto the EQ-5D index using a number of different model specifications. The predicted EQ-5D scores for subsets of the sample are compared across inpatient and outpatient settings and medical conditions. This paper compares the results to those obtained from existing mapping functions.

Results: The model including SF-36 dimensions, squared and interaction terms estimated using random effects GLS has the most accurate predictions of all models estimated here and existing mapping functions as indicated by MAE (0.127) and MSE (0.030). Mean absolute error in predictions by EQ-5D utility range increases with severity for our models (0.085 to 0.34) and for existing mapping functions (0.123 to 0.272).

Conclusion: Our results suggest that models mapping the SF-36 onto the EQ-5D have similar predictions across inpatient and outpatient setting and medical conditions. However, the models overpredict for more severe EQ-5D states; this problem is also present in the existing mapping functions.

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Observed and predicted EQ-5D scores: Using EQ-5D tariff re-estimated without an N3 term using the MVH data.  EQ-5D score  Reestimated EQ-5D score  Predictions using reestimated EQ-5D score
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Figure 3: Observed and predicted EQ-5D scores: Using EQ-5D tariff re-estimated without an N3 term using the MVH data. EQ-5D score Reestimated EQ-5D score Predictions using reestimated EQ-5D score

Mentions: One hypothesis is that the predictions may be poor for more severe EQ-5D states because they all have at least one dimension at the most severe level and the EQ-5D model uses an 'N3' term, a dummy variable for states with at least one dimension at the most severe level. The 'N3' term was used in the original UK modelling [6], but has not been included in all the models of other EQ-5D valuation studies (see for example the US valuation study, [16]). The inclusion of the N3 term may be a reason why the utility score is overpredicted for the more severe states which have at least one dimension at the most severe level. We re-estimated the EQ-5D tariff without the N3 term using the same data and methods as the original UK tariff [6]. The re-estimated tariff and the original UK tariff [6] produce similar scores for mild and very severe health states but deviate for more moderate health states, with mean difference in tariff values at the state level of 0.134 and mean squared difference of 0.026. Figure 3 plots the observed and predicted EQ-5D utility scores using a re-estimated version of the EQ-5D and plots this alongside the UK tariff values [6]. The predicted values for the re-estimated EQ-5D scores still overpredict for more severe states, but not as much as previously, with MAE of 0.106 and MSE of 0.021 in comparison to MAE of 0.127 and MSE of 0.030 for the predictions based on the UK tariff [6]. However the PITS state is overpredicted by 0.63 for the re-estimated EQ-5D scores and 0.61 for the predictions based on the UK tariff [6].


Mapping SF-36 onto the EQ-5D index: how reliable is the relationship?

Rowen D, Brazier J, Roberts J - Health Qual Life Outcomes (2009)

Observed and predicted EQ-5D scores: Using EQ-5D tariff re-estimated without an N3 term using the MVH data.  EQ-5D score  Reestimated EQ-5D score  Predictions using reestimated EQ-5D score
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2683169&req=5

Figure 3: Observed and predicted EQ-5D scores: Using EQ-5D tariff re-estimated without an N3 term using the MVH data. EQ-5D score Reestimated EQ-5D score Predictions using reestimated EQ-5D score
Mentions: One hypothesis is that the predictions may be poor for more severe EQ-5D states because they all have at least one dimension at the most severe level and the EQ-5D model uses an 'N3' term, a dummy variable for states with at least one dimension at the most severe level. The 'N3' term was used in the original UK modelling [6], but has not been included in all the models of other EQ-5D valuation studies (see for example the US valuation study, [16]). The inclusion of the N3 term may be a reason why the utility score is overpredicted for the more severe states which have at least one dimension at the most severe level. We re-estimated the EQ-5D tariff without the N3 term using the same data and methods as the original UK tariff [6]. The re-estimated tariff and the original UK tariff [6] produce similar scores for mild and very severe health states but deviate for more moderate health states, with mean difference in tariff values at the state level of 0.134 and mean squared difference of 0.026. Figure 3 plots the observed and predicted EQ-5D utility scores using a re-estimated version of the EQ-5D and plots this alongside the UK tariff values [6]. The predicted values for the re-estimated EQ-5D scores still overpredict for more severe states, but not as much as previously, with MAE of 0.106 and MSE of 0.021 in comparison to MAE of 0.127 and MSE of 0.030 for the predictions based on the UK tariff [6]. However the PITS state is overpredicted by 0.63 for the re-estimated EQ-5D scores and 0.61 for the predictions based on the UK tariff [6].

Bottom Line: This paper compares the results to those obtained from existing mapping functions.The model including SF-36 dimensions, squared and interaction terms estimated using random effects GLS has the most accurate predictions of all models estimated here and existing mapping functions as indicated by MAE (0.127) and MSE (0.030).Our results suggest that models mapping the SF-36 onto the EQ-5D have similar predictions across inpatient and outpatient setting and medical conditions.

View Article: PubMed Central - HTML - PubMed

Affiliation: Health Economics and Decision Science, University of Sheffield, Regent Court, 30 Regent Street, Sheffield, S1 4DA, UK. d.rowen@sheffield.ac.uk

ABSTRACT

Background: Mapping from health status measures onto generic preference-based measures is becoming a common solution when health state utility values are not directly available for economic evaluation. However the accuracy and reliability of the models employed is largely untested, and there is little evidence of their suitability in patient datasets. This paper examines whether mapping approaches are reliable and accurate in terms of their predictions for a large and varied UK patient dataset.

Methods: SF-36 dimension scores are mapped onto the EQ-5D index using a number of different model specifications. The predicted EQ-5D scores for subsets of the sample are compared across inpatient and outpatient settings and medical conditions. This paper compares the results to those obtained from existing mapping functions.

Results: The model including SF-36 dimensions, squared and interaction terms estimated using random effects GLS has the most accurate predictions of all models estimated here and existing mapping functions as indicated by MAE (0.127) and MSE (0.030). Mean absolute error in predictions by EQ-5D utility range increases with severity for our models (0.085 to 0.34) and for existing mapping functions (0.123 to 0.272).

Conclusion: Our results suggest that models mapping the SF-36 onto the EQ-5D have similar predictions across inpatient and outpatient setting and medical conditions. However, the models overpredict for more severe EQ-5D states; this problem is also present in the existing mapping functions.

Show MeSH