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Randomized Controlled Trials 3: Measurement and Analysis of Patient-Reported Outcomes
Michelle M Richardson1,2, Klemens B Meyer3,4
1William B. Schwartz Division of Nephrology, Tufts Medical Center, Boston, MA, USA.
Abstract:
The study of patient-reported outcomes, now common in clinical research, had its origins in social and scientific developments during the latter twentieth century. Patient-reported outcomes comprise functional and health status, health-related quality of life, and quality of life. The terms overlap and are used inconsistently, and these terms should be distinguished from expressions of preference regarding health states. Regulatory standards from the USA and European Union provide some guidance regarding reporting of patient-reported outcomes. Determining that patient-reported outcomes measurement is important depends in part on the balance between subjective and objective outcomes of the health problem under study. Instrument selection depends to a large extent on practical considerations. A number of instruments can be identified that are frequently used in particular clinical situations. The domain coverage of commonly used generic short forms varies substantially. Individualized measurement of quality of life is possible, but resource intensive. Focus groups are useful, not only for scale development but also to confirm the appropriateness of existing instruments.Under classical test theory, validity and reliability are the critical characteristics of tests. Under item response theory, validity remains central, but the focus moves from the reliability of scales to the relative levels of traits in individuals and items' relative difficulty. Plans for clinical studies should include an explicit model of the relationship of patient-reported outcomes to other parameters, as well as define the magnitude of difference in patient-reported outcomes that will be considered important. It is particularly important to minimize missing patient-reported outcome data; to a limited extent, a variety of statistical techniques can mitigate the consequences of missing data.
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