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      Which health-related quality of life score? A comparison of alternative utility measures in patients with Type 2 diabetes in the ADVANCE trial

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      1 , , 2 , 2 , 3 , the ADVANCE Collaborative Group
      Health and Quality of Life Outcomes
      BioMed Central

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          Abstract

          Background

          Diabetes has a high burden of illness both in life years lost and in disability through related co-morbidities. Accurate assessment of the non-mortality burden requires appropriate health-related quality of life and summary utility measures of which there are several contenders. The study aimed to measure the impact of diabetes on various health-related quality of life domains, and compare several summary utility measures.

          Methods

          In the ADVANCE (Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation) study, 978 Australian patients with Type 2 diabetes completed two health-related quality of life questionnaires at baseline: the EQ-5D and the SF-36v2, from which nine summary utility measures were calculated, and compared. The algorithms were grouped into four classes: (i) based on the EQ-5D; (ii) using fewer items than those in the SF-12 (iii) using the items in the SF-12; and (iv) using all items of the SF-36.

          Results

          Overall health-related quality of life of the subjects was good (mean utility ranged from 0.68 (±0.08) to 0.85(±0.14) over the nine utility measures) and comparable to patients without diabetes. Summary indices were well correlated with each other (r = 0.76 to 0.99), and showed lower health-related quality of life in patients with major diabetes-related events such as stroke or myocardial infarction. Despite the smaller number of items used in the scoring of the EQ-5D, it generally performed at least as well as SF-36 based methods. However, all utility measures had some limitation such as limited range or ceiling effects.

          Conclusion

          The summary utility measures showed good agreement, and showed good discrimination between major and minor health state changes. However, EQ-5D based measures performed as well and are generally simpler to use.

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          Most cited references19

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          The estimation of a preference-based measure of health from the SF-12.

          The SF-12 is a multidimensional generic measure of health-related quality of life. It has become widely used in clinical trials and routine outcome assessment because of its brevity and psychometric performance, but it cannot be used in economic evaluation in its current form. We sought to derive a preference-based measure of health from the SF-12 for use in economic evaluation and to compare it with the original SF-36 preference-based index. The SF-12 was revised into a 6-dimensional health state classification (SF-6D [SF-12]) based on an item selection process designed to ensure the minimum loss of descriptive information. A sample of 241 states defined by the SF-6D (of 7500) have been valued by a representative sample of 611 members of the UK general population using the standard gamble (SG) technique. Models are estimated of the relationship between the SF-6D (SF-12) and SG values and evaluated in terms of their coefficients, overall fit, and the ability to predict SG values for all health states. The models have produced significant coefficients for levels of the SF-6D (SF-12), which are robust across model specification. The coefficients are similar to those of the SF-36 version and achieve similar levels of fit. There are concerns with some inconsistent estimates and these have been merged to produce the final recommended model. As for the SF-36 model, there is evidence of over prediction of the value of the poorest health states. The SF-12 index provides a useful tool for researchers and policy makers wishing to assess the cost-effectiveness of interventions.
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            The estimation of a preference-based measure of health from the SF-36.

            This paper reports on the findings of a study to derive a preference-based measure of health from the SF-36 for use in economic evaluation. The SF-36 was revised into a six-dimensional health state classification called the SF-6D. A sample of 249 states defined by the SF-6D have been valued by a representative sample of 611 members of the UK general population, using standard gamble. Models are estimated for predicting health state valuations for all 18,000 states defined by the SF-6D. The econometric modelling had to cope with the hierarchical nature of the data and its skewed distribution. The recommended models have produced significant coefficients for levels of the SF-6D, which are robust across model specification. However, there are concerns with some inconsistent estimates and over prediction of the value of the poorest health states. These problems must be weighed against the rich descriptive ability of the SF-6D, and the potential application of these models to existing and future SF-36 data set.
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              US valuation of the EQ-5D health states: development and testing of the D1 valuation model.

              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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                Author and article information

                Journal
                Health Qual Life Outcomes
                Health and Quality of Life Outcomes
                BioMed Central (London )
                1477-7525
                2007
                27 April 2007
                : 5
                : 21
                Affiliations
                [1 ]Centre for Evidence-Based Practice, Institute of Health Sciences, Oxford University, Oxford OX3 7LF, UK
                [2 ]Queensland Clinical Trials Centre, University of Queensland, Australia
                [3 ]School of Public Health, University of Sydney, Australia
                Article
                1477-7525-5-21
                10.1186/1477-7525-5-21
                1950473
                17462100
                ff64df0c-1282-43fd-b3cb-83e3c30c9cfd
                Copyright © 2007 Glasziou et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 28 November 2006
                : 27 April 2007
                Categories
                Research

                Health & Social care
                Health & Social care

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