Skip to main content
Mijn bibliotheek
Shop
Nieuws Boeken Tijdschriften E-learning Web-tv Video Audio
Tips voor Mijn BSL
UITGEBREID ZOEKEN
Inloggen
Top
Quality of Life Research
Gepubliceerd in: Quality of Life Research 1/2007

01-08-2007 | Editorial notes

Applying item response theory to enhance health outcomes assessment

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

Gepubliceerd in: Quality of Life Research | bijlage 1/2007

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail
    Link delen
publish
Publiceer nu

Excerpt

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. …
volgende artikel Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement
Literatuur
1.
Lipscomb, J., Donaldson, M. S., & Hiatt, R. A. (2004). Cancer outcomes research and the arenas of application. JNCI Monographs, 33, 1–7.CrossRef
2.
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.
3.
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.
4.
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.
5.
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.
6.
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
7.
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.
8.
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
Metagegevens
Titel
Applying item response theory to enhance health outcomes assessment
Auteurs
Bryce B. Reeve
Ron D. Hays
Chih-Hung Chang
Eleanor M. Perfetto
Publicatiedatum
01-08-2007
Uitgeverij
Springer Netherlands
Gepubliceerd in
Quality of Life Research / Uitgave bijlage 1/2007
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-007-9220-6

Andere artikelen bijlage 1/2007

Quality of Life Research 1/2007 Naar de uitgave

Original Paper

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

Original Paper

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

OriginalPaper

Patient-reported outcomes measurement and management with innovative methodologies and technologies

Original Paper

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

OriginalPaper

Applying item response theory and computer adaptive testing: the challenges for health outcomes assessment

Original Paper

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