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Quality of Life Research

Erschienen in:

01.08.2007 | Editorial notes

Applying item response theory to enhance health outcomes assessment

verfasst von: Bryce B. Reeve, Ron D. Hays, Chih-Hung Chang, Eleanor M. Perfetto

Erschienen in: Quality of Life Research | Sonderheft 1/2007

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In June 2004, the National Cancer Institute and Drug Information Association co-sponsored a conference focused on developing patient-reported outcome (PRO) questionnaires, analyzing data collected from patients, and utilizing findings to enhance decision making for treatment and health policy, “Advances in Health Outcomes Measurement: Exploring the Current State and the Future of Item Response Theory (IRT), Item Banks, and Computer-Adaptive Testing.” Invited speakers were internationally recognized experts in psychometrics and health-outcomes measurement. Their rich experiences are reflected in the articles in this supplement that focus on describing the methods of modern measurement theory and its applications, as well as challenges for improving PRO measures such as pain, fatigue, physical function, and emotional distress. …

Nächster Artikel Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement

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Lipscomb, J., Donaldson, M. S., & Hiatt, R. A. (2004). Cancer outcomes research and the arenas of application. JNCI Monographs, 33, 1–7.CrossRef

Gotay, C. C., Lipscomb, J., & Snyder, C. F. (2005). Reflections on COMWG findings and moving to the next phase. In: J. Lipscomb, C. C. Gotay & C. F. Snyder (Eds.), Outcomes assessment in cancer: Measures, methods, and applications (pp. 568–583). Cambridge, UK: Cambridge University Press.

Scientific Advisory Committee of the Medical Outcomes Trust. (2002). Assessing health status and quality-of-life instruments: Attributes and review criteria. QOL Research, 11, 193–205.

Fries, J. (2005, April). The promise of PROMIS: Reducing sample sizes for clinical trials. Paper presented at the meeting of the National Institute for Health and Clinical Excellence (NICE), London, England.

Thissen, D., Steinberg, L., & Wainer, H. (1993). Detection of differential item functioning using the parameters of item response models. In: P. W. Holland & H. Wainer (Eds.), Differential item functioning (pp. 67–113). Hillsdale, NJ: Lawrence Erlbaum Associates.

Bjorner, J. B., Kosinski, M., & Ware, J. E. Jr. (2003). Calibration of an item pool for assessing the burden of headaches: An application of item response theory to the headache impact test (HIT). Quality of Life Research, 12, 913–933.PubMedCrossRef

Chang, C.-H., & Cella, D. (1997). Equating health-related quality of life instruments in applied oncology settings. Physical Medicine and Rehabilitation: States of the Art Reviews, 11(2), 397–406.

Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., et al. (2007). The Patient-Reported Outcomes Measurement Information System (PROMIS): Progress of an NIH roadmap cooperative group during its first two years. Medical Care, 45, S3–S11PubMedCrossRef

Titel: Applying item response theory to enhance health outcomes assessment
verfasst von: Bryce B. Reeve
Ron D. Hays
Chih-Hung Chang
Eleanor M. Perfetto
Publikationsdatum: 01.08.2007
Verlag: Springer Netherlands
Erschienen in: Quality of Life Research / Ausgabe Sonderheft 1/2007
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-007-9220-6

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Weitere Artikel der Sonderheft 1/2007

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IRT health outcomes data analysis project: an overview and summary

A comparison of three sets of criteria for determining the presence of differential item functioning using ordinal logistic regression

Developing tailored instruments: item banking and computerized adaptive assessment

Next steps for use of item response theory in the assessment of health outcomes

The future of outcomes measurement: item banking, tailored short-forms, and computerized adaptive assessment