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. 2013 Aug;33(6):793-803.
doi: 10.1177/0272989X13482524. Epub 2013 Apr 29.

US valuation of the SF-6D

Benjamin M Craig  1   2 , A Simon Pickard  3 , Elly Stolk  4 , John E Brazier  5
Affiliations

US valuation of the SF-6D

Benjamin M Craig et al. Med Decis Making. 2013 Aug.

Abstract

Background: The original SF-6D valuation study collected 3503 standard gambled responses from 611 UK respondents to predict quality-adjusted life year (QALY) values.

Methods: Using 19,980 paired comparison responses from 666 US respondents and a stacked probit model, the 25 coefficients of the original SF-6D multiattribute utility (MAU) regression were estimated, such that each coefficient represents a QALY decrement. The US QALY predictions were compared with UK predictions using 8428 SF-6D states in the US Medicare Health Outcomes Survey (MHOS), 1998 to 2003.

Results: Twenty-two of the 25 decrements in the SF-6D MAU regression are statistically significant. The remaining decrements are insignificant based on US and UK results. The US and UK QALY predictions for the MHOS SF-6D states are remarkably similar given differences in experimental design, format, and sampling (Lin's coefficient of agreement, 0.941; absolute mean difference, 0.043). Limitations. The underlying theoretical framework for the

Study design: and econometric analysis builds from the episodic random utility model and the concept of QALYs and inherits their limitations.

Conclusions: This study enhances the potential for US comparative effectiveness research by translating SF-6D states into US QALYs as well as improves upon discrete choice experiment design and econometric methods for health valuation.

Keywords: decision analysis; econometric methods; mathematical models; preferences and quality of life; statistical methods.

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Figures

Figure 1
Figure 1
Participation Flow Chart
Figure 2
Figure 2
US and UK SF-6D QALY Predictions* * Line represents a 45 degree angle demarcating the equivalence between US and UK QALY predictions
See this image and copyright information in PMC

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