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      The COSMIN checklist for evaluating the methodological quality of studies on measurement properties: A clarification of its content

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          Abstract

          Background

          The COSMIN checklist (COnsensus-based Standards for the selection of health status Measurement INstruments) was developed in an international Delphi study to evaluate the methodological quality of studies on measurement properties of health-related patient reported outcomes (HR-PROs). In this paper, we explain our choices for the design requirements and preferred statistical methods for which no evidence is available in the literature or on which the Delphi panel members had substantial discussion.

          Methods

          The issues described in this paper are a reflection of the Delphi process in which 43 panel members participated.

          Results

          The topics discussed are internal consistency (relevance for reflective and formative models, and distinction with unidimensionality), content validity (judging relevance and comprehensiveness), hypotheses testing as an aspect of construct validity (specificity of hypotheses), criterion validity (relevance for PROs), and responsiveness (concept and relation to validity, and (in) appropriate measures).

          Conclusions

          We expect that this paper will contribute to a better understanding of the rationale behind the items, thereby enhancing the acceptance and use of the COSMIN checklist.

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          Most cited references12

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          Coefficient alpha and the internal structure of tests

          Psychometrika, 16(3), 297-334
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            Individual-patient monitoring in clinical practice: are available health status surveys adequate?

            Interest has increased in recent years in incorporating health status measures into clinical practice for use at the individual-patient level. We propose six measurement standards for individual-patient applications: (1) practical features, (2) breadth of health measured, (3) depth of health measured, (4) precision for cross-sectional assessment, (5) precision for longitudinal monitoring and (6) validity. We evaluate five health status surveys (Functional Status Questionnaire, Dartmouth COOP Poster Charts, Nottingham Health Profile, Duke Health Profile, and SF-36 Health Survey) that have been proposed for use in clinical practice. We conducted an analytical literature review to evaluate the six measurement standards for individual-patient applications across the five surveys. The most problematic feature of the five surveys was their lack of precision for individual-patient applications. Across all scales, reliability standards for individual assessment and monitoring were not satisfied, and the 95% CIs were very wide. There was little evidence of the validity of the five surveys for screening, diagnosing, or monitoring individual patients. The health status surveys examined in this paper may not be suitable for monitoring the health and treatment status of individual patients. Clinical usefulness of existing measures might be demonstrated as clinical experience is broadened. At this time, however, it seems that new instruments, or adaptation of existing measures and scaling methods, are needed for individual-patient assessment and monitoring.
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              Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes.

              Our model proposes a taxonomy or classification scheme for different measures of health outcome. We divide these outcomes into five levels: biological and physiological factors, symptoms, functioning, general health perceptions, and overall quality of life. In addition to classifying these outcome measures, we propose specific causal relationships between them that link traditional clinical variables to measures of HRQL. As one moves from left to right in the model, one moves outward from the cell to the individual to the interaction of the individual as a member of society. The concepts at each level are increasingly integrated and increasingly difficult to define and measure. AT each level, there are an increasing number of inputs that cannot be controlled by clinicians or the health care system as it is traditionally defined.
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                Author and article information

                Journal
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central
                1471-2288
                2010
                18 March 2010
                : 10
                : 22
                Affiliations
                [1 ]Department of Epidemiology and Biostatistics and the EMGO Institute for Health and Care Research, VU University Medical Center, Amsterdam, the Netherlands
                [2 ]School of Rehabilitation Science and Department of Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, Canada
                [3 ]Health Services Research Unit, Institute Municipal d'Investigació Mèdica (IMIM-Hospital del Mar), Barcelona, Spain
                [4 ]Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Spain
                [5 ]Department of Health Services, University of Washington, Seattle, USA
                [6 ]Executive Board of VU University Amsterdam, Amsterdam, the Netherlands
                Article
                1471-2288-10-22
                10.1186/1471-2288-10-22
                2848183
                20298572
                c40d0e89-e0ba-4dc3-ad2e-6bc129c97e75
                Copyright ©2010 Mokkink 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
                : 18 August 2009
                : 18 March 2010
                Categories
                Correspondence

                Medicine
                Medicine

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