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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 the US-based EQ-5D tariff.  EQ-5D score  US-based tariff EQ-5D score  Predictions using US-based tariff
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Figure 4: Observed and predicted EQ-5D scores: Using the US-based EQ-5D tariff. EQ-5D score US-based tariff EQ-5D score Predictions using US-based tariff

Mentions: The re-estimated UK tariff and the UK tariff [6] produce similar scores for mild and very severe health states and hence the preferences regarding more severe health states may be a property of the dataset rather than the estimation technique used for the valuation. The US-based EQ-5D tariff has a smaller range from 1 to -0.11 and hence has higher scores for very severe states, suggesting that the mapping relationship between the US-based EQ-5D index and the SF-36 may not suffer from overprediction for more severe health states. Figure 4 plots the observed and predicted EQ-5D scores using the US-based tariff values [16] alongside the UK tariff values [6]. This demonstrates that the predicted values for the US-based EQ-5D values still overpredict for more severe states, but the estimates are more reliable than those plotted in figure 3 with MAE of 0.110 and MSE of 0.022 in comparison to MAE of 0.127 and MSE of 0.030 for the predictions based on UK tariff [6]. The PITS state is overpredicted by 0.38 for the US-based EQ-5D values and 0.86 for the predictions based on 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 the US-based EQ-5D tariff.  EQ-5D score  US-based tariff EQ-5D score  Predictions using US-based tariff
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: Observed and predicted EQ-5D scores: Using the US-based EQ-5D tariff. EQ-5D score US-based tariff EQ-5D score Predictions using US-based tariff
Mentions: The re-estimated UK tariff and the UK tariff [6] produce similar scores for mild and very severe health states and hence the preferences regarding more severe health states may be a property of the dataset rather than the estimation technique used for the valuation. The US-based EQ-5D tariff has a smaller range from 1 to -0.11 and hence has higher scores for very severe states, suggesting that the mapping relationship between the US-based EQ-5D index and the SF-36 may not suffer from overprediction for more severe health states. Figure 4 plots the observed and predicted EQ-5D scores using the US-based tariff values [16] alongside the UK tariff values [6]. This demonstrates that the predicted values for the US-based EQ-5D values still overpredict for more severe states, but the estimates are more reliable than those plotted in figure 3 with MAE of 0.110 and MSE of 0.022 in comparison to MAE of 0.127 and MSE of 0.030 for the predictions based on UK tariff [6]. The PITS state is overpredicted by 0.38 for the US-based EQ-5D values and 0.86 for the predictions based on 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