Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research
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This article focuses on the necessary psychometric properties of a patient-reported outcomes (PROs) measure. Topics include the importance of reliability and validity, psychometric approaches used to provide reliability and validity estimates, the kinds of evidence needed to indicate that a PRO has a sufficient level of reliability and validity, contexts that may affect psychometric properties, methods available to evaluate PRO instruments when the context varies, and types of reliability and validity testing that are appropriate during different phases of clinical trials. Points discussed include the perspective that the psychometric properties of reliability and validity are on a continuum in which the more evidence one has, the greater confidence there is in the value of the PRO data. ⋯ Psychometric testing ideally occurs before the initiation of Phase III trials. When testing does not occur prior to a Phase III trial, considerable risk is posed in relation to the ability to substantiate the use of the PRO data. Various qualitative (e.g., focus groups, behavioral coding, cognitive interviews) and quantitative approaches (e.g., differential item functioning testing) are useful in evaluating the reliability and validity of PRO instruments.
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This article deals with the incorporation of patient-reported outcomes (PROs) into clinical trials and focuses on issues associated with the interpretation and reporting of PRO data. The primary focus and context of this information relates to the evidentiary support and reporting for a labeling or advertising claim of a PRO benefit for a new or approved pharmaceutical product. ⋯ Clear specifications for considering a finding on a PRO measure, as clinically meaningful, need to be determined by instrument developers and psychometricians; they need to be reported for all clinical trials involving PRO end points. Clinical trial reports need to be comprehensive, clear, and sufficient to enable any reader to understand the methods, PRO measures, statistical analysis, and results.
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Health decision-makers involved with coverage and payment policies are increasingly developing policies that seek information on "real-world" (RW) outcomes. Motivated by these initiatives, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) created a Task Force on Real-World Data to develop a framework to assist health-care decision-makers in dealing with RW data, especially related to coverage and payment decisions. ⋯ Real-world data are essential for sound coverage and reimbursement decisions. The types and applications of such data are varied, and context matters greatly in determining the value of a particular type in any circumstance. It is critical that policymakers recognize the benefits, limitations, and methodological challenges in using RW data, and the need to consider carefully the costs and benefits of different forms of data collection in different situations.
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There is growing recognition that a comprehensive economic assessment of a new health-care intervention at the time of launch requires both a cost-effectiveness analysis (CEA) and a budget impact analysis (BIA). National regulatory agencies such as the National Institute for Health and Clinical Excellence in England and Wales and the Pharmaceutical Benefits Advisory Committee in Australia, as well as managed care organizations in the United States, now require that companies submit estimates of both the cost-effectiveness and the likely impact of the new health-care interventions on national, regional, or local health plan budgets. Although standard methods for performing and presenting the results of CEAs are well accepted, the same progress has not been made for BIAs. The objective of this report is to present guidance on methodologies for those undertaking such analyses or for those reviewing the results of such analyses. ⋯ The BIA is important, along with the CEA, as part of a comprehensive economic evaluation of a new health technology. We propose a framework for creating budget impact models, guidance about the acquisition and use of data to make budget projections and a common reporting format that will promote standardization and transparency. Adherence to these proposed good research practice principles would not necessarily supersede jurisdiction-specific budget impact guidelines, but may support and enhance local recommendations or serve as a starting point for payers wishing to promulgate methodology guidelines.
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Using patient and expert provider input, we previously developed a 15-item index of the most important symptoms and concerns of people being treated for advanced kidney cancer, the Functional Assessment of Cancer Therapy - Kidney Symptom Index (FKSI). These 15 concerns are a mixture of disease-related symptoms and treatment-related side effects. As a result, it may be difficult to assign an informative label to the score defined as the summation of these 15 most important concerns. Because one of the primary goals of treating advanced kidney cancer is the relief of disease-related symptoms, we set out to differentiate from the list of 15 symptoms those that are predominantly attributable to kidney cancer itself rather than its treatment, and to evaluate this abbreviated FKSI - Disease-Related Symptoms (FKSI-DRS). ⋯ The FKSI-DRS is a reliable, valid, and responsive brief index of the most important symptoms associated with advanced kidney cancer.