- •Despite the vast amount of real-life data accumulated in healthcare, EQ-5D index scores are frequently lacking for health economic analyses and have to be estimated.
- •We predicted 3 level version of EQ-5D (EQ-5D-3L) index scores in a large heterogenous data set of population surveys and clinical studies using eXtreme Gradient Boosting classification, eXtreme Gradient Boosting regression, and ordinary least squares regression. Regression methods outperformed classification in terms of prediction accuracy and bias. The performance of the 3 methods depended on the applied evaluation criteria, the target population, the included predictors, and the EQ-5D-3L index score range.
- •The prediction accuracy of individual EQ-5D-3L index scores was inadequate for the majority of respondents. For the evaluation of personalized health interventions, we encourage the systematic collection of patient-reported outcomes such as EQ-5D with the involvement of artificial intelligence experts and outcomes researchers to enhance the value of accumulating data in health systems.
- Berwick D.
- Black N.
- Cullen D.
- et al.
- Berwick D.
- Black N.
- Cullen D.
- et al.
National Centre for Healthcare Services.
- Offerman A.
- Donaldson C.
- Baker R.
- Mason H.
- et al.
- Donaldson C.
- Baker R.
- Mason H.
- et al.
Database and Variables
Demographic and disease-related variables
Evaluation of Model Performance
where is the mean and SD is the SD of the evaluation metric (eg, MAE) of the k cross-validation sets.
|General population (“Pop”)||Patients (“Pts”)||Entire sample (“Total”)|
|Place of residence||Capital (%)||18.1||6.2||13.5|
|Family status||Single (%)||36.2||35.0||36.1|
|Employment||Paid employment (%)||68.6||42.9||61.6|
|Not working (%)||5.0||6.7||5.5|
|Other employment (%)||2.2||3.9||2.6|
|Relative income (0-11.0)||Mean||1.6||0.5||1.6|
|Setting||General population (%)||100.0||-||69.7|
|Number of GP visits at 12 months||Mean||-||4.0||4.0|
|Any GP visit past year||No (%)||-||49.1||49.1|
|Specialist visits past year||Mean||-||5.8||5.8|
|Any specialist visits past year||No (%)||-||17.1||17.1|
|Hospitalizations past year||Mean||0.2||1.7||0.7|
|Any hospitalization at 12 months||No (%)||90.4||41.9||72.5|
|Informal care recipient||No (%)||92.2||70.9||82.4|
|Chronic morbidity||No (%)||68.5||0.0||68.5|
|Any disease||No (%)||70.5||0.0||29.5|
|Specific diagnoses||Not included in the table|
|Self-rated health||Very good (%)||20.7||0.0||20.7|
|Very Bad (%)||0.9||0.0||0.9|
|GALI||Severely limited (%)||3.3||0.0||3.3|
|Limited, but not severely (%)||16.8||0.0||16.8|
|Not limited (%)||79.9||0.0||79.9|
The Distribution of True and Predicted EQ-5D-3L Index Scores
Accuracy of Predictions
Patterns of Prediction Error
Accuracy of Predictions by True EQ-5D-3L Index Scores
Accuracy of Predictions by Predicted EQ-5D-3L Index Scores
- Borchani H.
- Bielza C.
- Marti Nez-Marti N.P.
- Larranaga P.
- Borchani H.
- Bielza C.
- Marti Nez-Marti N.P.
- Larranaga P.
Article and Author Information
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