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Med Decis Making. 2011 May-Jun;31(3):481-93. doi: 10.1177/0272989X10386800. Epub 2010 Dec 2.

Predicting EQ-5D utility scores from the Seattle Angina Questionnaire in coronary artery disease: a mapping algorithm using a Bayesian framework.

Author information

1
Division of Cardiology, Schulich Heart Centre and Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Ontario, Canada (HCW, DTK)
2
Toronto Health Economics and Technology Assessment (THETA) Collaborative, University of Toronto (HCW, GT, MDK)
3
Department of Medicine, University of Toronto (HCW, GT, DTK, MDK)
4
Department of Health Policy, Management and Evaluation, University of Toronto (HCW, GT, DTK, MDK)
5
Faculty of Nursing, University of Alberta, Canada (CMN)
6
Division of Cardiology, Cardiovascular Surgery and Public Health, University of Alberta, Canada (CMN)
7
University of Calgary, Canada (WAG)
8
Institute for Clinical Evaluative Sciences, Ontario, Canada (DTK)
9
Faculty of Pharmacy, University of Toronto (MDK)

Abstract

BACKGROUND:

The Seattle Angina Questionnaire (SAQ), a descriptive quality of life instrument, is often used in coronary artery disease studies. In its current form, however, it cannot be used in economic evaluations. The investigators sought to create a mapping algorithm that would allow translation of SAQ scores into EQ-5D utility scores.

METHODS:

Data from the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database were used to examine the relationship between scores in each of the 5 domains of the SAQ (physical limitation, anginal stability, anginal frequency, treatment satisfaction, and disease perception) and the EQ-5D utility score. The cohort was divided into 80% derivation and 20% validation sets. Mapping algorithms were developed using simple linear regression and Tobit models. To account for the skewed distribution of the EQ-5D scores and the presence of heteroscedasticity, Bayesian extensions were applied to each model by specifying a nonconstant variance for the error term. Model performance was assessed by comparing predicted and observed mean EQ-5D scores in the validation set, and the unadjusted R2.

RESULTS:

The cohort consisted of 1992 patients. The simple linear regression model had the best predictive performance, with an R2 of 0.38. The nonconstant variance term did not improve overall performance for any of the models. The linear regression model accurately estimated the mean EQ-5D score in the validation set (predicted score 0.81 v. observed score 0.81).

CONCLUSIONS:

Mean EQ-5D utility weights can be accurately estimated from the SAQ using a simple linear regression mapping algorithm.

PMID:
21127316
DOI:
10.1177/0272989X10386800
[Indexed for MEDLINE]

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