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      Estimation of minimally important differences in EQ-5D utility and VAS scores in cancer

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      1 , , 2 , 3
      Health and Quality of Life Outcomes
      BioMed Central

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          Abstract

          Background

          Understanding what constitutes an important difference on a HRQL measure is critical to its interpretation. The aim of this study was to provide a range of estimates of minimally important differences (MIDs) in EQ-5D scores in cancer and to determine if estimates are comparable in lung cancer.

          Methods

          A retrospective analysis was conducted on cross-sectional data collected from 534 cancer patients, 50 of whom were lung cancer patients. A range of minimally important differences (MIDs) in EQ-5D index-based utility (UK and US) scores and VAS scores were estimated using both anchor-based and distribution-based (1/2 standard deviation and standard error of the measure) approaches. Groups were anchored using Eastern Cooperative Oncology Group performance status (PS) ratings and FACT-G total score-based quintiles.

          Results

          For UK-utility scores, MID estimates based on PS ranged from 0.10 to 0.12 both for all cancers and for lung cancer subgroup. Using FACT-G quintiles, MIDs were 0.09 to 0.10 for all cancers, and 0.07 to 0.08 for lung cancer. For US-utility scores, MIDs ranged from 0.07 to 0.09 grouped by PS for all cancers and for lung cancer; when based on FACT-G quintiles, MIDs were 0.06 to 0.07 in all cancers and 0.05 to 0.06 in lung cancer. MIDs for VAS scores were similar for lung and all cancers, ranging from 8 to 12 (PS) and 7 to 10 (FACT-G quintiles).

          Discussion

          Important differences in EQ-5D utility and VAS scores were similar for all cancers and lung cancer, with the lower end of the range of estimates closer to the MID, i.e. 0.08 for UK-index scores, 0.06 for US-index scores, and 0.07 for VAS scores.

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          Most cited references14

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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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            Minimal changes in health status questionnaires: distinction between minimally detectable change and minimally important change

            Changes in scores on health status questionnaires are difficult to interpret. Several methods to determine minimally important changes (MICs) have been proposed which can broadly be divided in distribution-based and anchor-based methods. Comparisons of these methods have led to insight into essential differences between these approaches. Some authors have tried to come to a uniform measure for the MIC, such as 0.5 standard deviation and the value of one standard error of measurement (SEM). Others have emphasized the diversity of MIC values, depending on the type of anchor, the definition of minimal importance on the anchor, and characteristics of the disease under study. A closer look makes clear that some distribution-based methods have been merely focused on minimally detectable changes. For assessing minimally important changes, anchor-based methods are preferred, as they include a definition of what is minimally important. Acknowledging the distinction between minimally detectable and minimally important changes is useful, not only to avoid confusion among MIC methods, but also to gain information on two important benchmarks on the scale of a health status measurement instrument. Appreciating the distinction, it becomes possible to judge whether the minimally detectable change of a measurement instrument is sufficiently small to detect minimally important changes.
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              Linking clinical relevance and statistical significance in evaluating intra-individual changes in health-related quality of life.

              To compare the standard error of measurement (SEM) with established standards for clinically relevant intra-individual change in an evaluation of health-related quality of life. Secondary analysis of data from a randomized controlled trial. Six hundred and five outpatients with a history of cardiac problems attending the general medicine clinics of a major academic medical center. Baseline and follow-up interviews included a modified version of the Chronic Heart Failure Questionnaire (CHQ) and the SF-36. The SEM values corresponding to established standards for minimal clinically important differences (MCIDs) on the CHQ were determined. Individual change on the SF-36 was explored using the same SEM criterion. One-SEM changes in this population corresponded well to the patient-driven MCID standards on all CHQ dimensions (weighted kappas (0.87; P < 0.001). The distributions of outpatients who improved, remained stable, or declined (defined by the one-SEM criterion) were generally consistent between CHQ dimensions and SF-36 subscales. The use of the SEM to evaluate individual patient change should be explored among other health-related quality of life instruments with established standards for clinically relevant differences. Only then can it be determined whether the one-SEM criterion can be consistently applied as a proxy for clinically meaningful change.
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                Author and article information

                Journal
                Health Qual Life Outcomes
                Health and Quality of Life Outcomes
                BioMed Central
                1477-7525
                2007
                21 December 2007
                : 5
                : 70
                Affiliations
                [1 ]Center for Pharmacoeconomic Research, Department of Pharmacy Practice, College of Pharmacy, University of Illinois at Chicago, Chicago, USA
                [2 ]Global Health Outcomes, GlaxoSmithKline, Collegeville, Pennsylvania, USA
                [3 ]Center for Outcomes Research and Education, Evanston Healthcare and Feinberg School of Medicine, Northwestern University, Chicago, USA
                Article
                1477-7525-5-70
                10.1186/1477-7525-5-70
                2248572
                18154669
                8e737147-fae4-419e-ad5e-9ff8c65065f0
                Copyright © 2007 Pickard 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
                : 27 August 2007
                : 21 December 2007
                Categories
                Research

                Health & Social care
                Health & Social care

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