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- Swaine-Verdier, A, et al.
(author)
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Adapting quality of life instruments
- 2004
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In: Value in Health. - : Elsevier BV. - 1098-3015. ; 7:s1, s. 27-30
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Journal article (peer-reviewed)abstract
- Due to the international nature of many clinical studies and trials it is often necessary to produce several language versions of specific measures. While it is generally acknowledged that it is necessary to produce versions that are conceptually equivalent, the best method of achieving this is more controversial. It is commonly stated that there is a gold-standard method, which involves forward and backward translation. However, no evidence has been presented to support this view. This paper argues that the "gold-standard" method is difficult to support and describes an alternative method involving dual translation panels that has been used in the production of all adaptations of needs-based quality of life instruments.
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| 17. |
- Tennant, A, et al.
(author)
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Application of Rasch analysis in the development and application of quality of life instruments
- 2004
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In: Value in Health. - : Elsevier BV. - 1098-3015. ; 7:Suppl. 1, s. 22-26
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Journal article (peer-reviewed)abstract
- This paper discusses recent advances that have been made in the field of psychometrics, specifically, the application of Rasch analysis to the instrument development process. It emphasizes the importance of assessing the fundamental scaling properties of an instrument prior to consideration of traditional psychometric indicators. The paper introduces Rasch analysis and shows how it has been applied in the development of needs-based measures in order to ensure that they provide unidimensional measurement. By ensuring that scales are based on the same measurement model and that they fit the Rasch model it is possible for QoL scores to be compared across diseases by means of cocalibration and item banking.
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- Weinstein, M.C., et al.
(author)
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Principles of Good Practice for Decision Analytic Modeling in Health-Care Evaluation: Report of the ISPOR Task Force on Good Research Practices—Modeling Studies
- 2003
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In: Value in health. - : Elsevier Inc. - 1524-4733 .- 1098-3015. ; 6:1, s. 9-17
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Journal article (peer-reviewed)abstract
- Mathematical modeling is used widely in economic evaluations of pharmaceuticals and other health-care technologies. Users of models in government and the private sector need to be able to evaluate the quality of models according to scientific criteria of good practice. This report describes the consensus of a task force convened to provide modelers with guidelines for conducting and reporting modeling studies. The task force was appointed with the advice and consent of the Board of Directors of ISPOR. Members were experienced developers or users of models, worked in academia and industry, and came from several countries in North America and Europe. The task force met on three occasions, conducted frequent correspondence and exchanges of drafts by electronic mail, and solicited comments on three drafts from a core group of external reviewers and more broadly from the membership of ISPOR. Criteria for assessing the quality of models fell into three areas: model structure, data used as inputs to models, and model validation. Several major themes cut across these areas. Models and their results should be represented as aids to decision making, not as statements of scientific fact; therefore, it is inappropriate to demand that models be validated prospectively before use. However, model assumptions regarding causal structure and parameter estimates should be continually assessed against data, and models should be revised accordingly. Structural assumptions and parameter estimates should be reported clearly and explicitly, and opportunities for users to appreciate the conditional relationship between inputs and outputs should be provided through sensitivity analyses. Model-based evaluations are a valuable resource for health-care decision makers. It is the responsibility of model developers to conduct modeling studies according to the best practicable standards of quality and to communicate results with adequate disclosure of assumptions and with the caveat that conclusions are conditional upon the assumptions and data on which the model is built.
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