US valuation of the EQ-5D health states: development and testing of the D1 valuation model.
ABSTRACT The EQ-5D is a brief, multiattribute, preference-based health status measure. This article describes the development of a statistical model for generating US population-based EQ-5D preference weights.
A multistage probability sample was selected from the US adult civilian noninstitutional population. Respondents valued 13 of 243 EQ-5D health states using the time trade-off (TTO) method. Data for 12 states were used in econometric modeling. The TTO valuations were linearly transformed to lie on the interval [-1, 1]. Methods were investigated to account for interaction effects caused by having problems in multiple EQ-5D dimensions. Several alternative model specifications (eg, pooled least squares, random effects) also were considered. A modified split-sample approach was used to evaluate the predictive accuracy of the models. All statistical analyses took into account the clustering and disproportionate selection probabilities inherent in our sampling design.
Our D1 model for the EQ-5D included ordinal terms to capture the effect of departures from perfect health as well as interaction effects. A random effects specification of the D1 model yielded a good fit for the observed TTO data, with an overall R of 0.38, a mean absolute error of 0.025, and 7 prediction errors exceeding 0.05 in absolute magnitude.
The D1 model best predicts the values for observed health states. The resulting preference weight estimates represent a significant enhancement of the EQ-5D's utility for health status assessment and economic analysis in the US.
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ABSTRACT: Objective: To identify the key methodological issues in the construction of population-level EQ-5D / Time Trade-Off (TTO) preference elicitation studies. Study Design: This study involves three components. The first was to identify existing population-level EQ-5D TTO studies. The second was to illustrate and discuss the key areas of divergence between studies, including the international comparison of tariffs. The third was to portray the relative merits of each of the approaches, and to compare the results of studies across countries. Results: While most papers report use of the protocol developed in the original UK study, we identified three key areas of divergence in the construction and analysis of surveys. These are the number of health states valued in order to determine the algorithm for estimating all health states, the approach to valuing states worse than immediate death, and the choice of algorithm. Finally, the evidence on international comparisons suggests differences between countries, although it is difficult to disentangle differences in cultural attitudes with random error and differences due to methodological divergence. Conclusion: Differences in methods are likely to obscure true differences in values between countries. However, population-specific valuation sets for countries engaging in economic evaluation would better represent societal attitudes.
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ABSTRACT: This study explores mechanisms involved in self-evaluation of health by making specifications of linkages among various dimensions of health status, physiological measures, social and behavioral factors or characteristics. The proposed structural equation model is tested by using data from a comprehensive health survey of the population of Finmark county, Norway (1987-1988), including 4549 men and 4360 women aged 30-62. The findings suggest the burden of physical distress and reliance on permanent disablement benefit to play the key role in reducing self-evaluated health. The seemingly strong labelling impact of permanent work disability, contrasted to modest effect of diagnoses of chronic disease. Moreover, the impact of both these key factors and other important determinants is strongly socially patterned. Positive health related life-style appeared to have a positive impact on self-rated health, while preoccupation with health had a negative impact. This finding adds some credibility to the suggestion that the growing occupation and fascination with health have some negative health outcomes.
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ABSTRACT: Multi-attribute utility-based instruments (MAUIs) assess health status and provide an index score on the full health-dead scale, and are widely used to support reimbursement decisions for new healthcare interventions worldwide. A valuation study is a key part of the development of MAUIs, with the primary goal of developing a scoring algorithm through eliciting societal preferences. We developed the 21-item Checklist for REporting VAluaTion StudiEs (CREATE) by following a modified two-round Delphi panel approach plus an email survey. CREATE is intended to promote good reporting practice as well as guiding developers to thoroughly and carefully think through key methodological elements in designing valuation studies.PharmacoEconomics 05/2015; DOI:10.1007/s40273-015-0292-9 · 3.34 Impact Factor