%0 Journal Article %J Medical Care %D 2007 %T Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS) %A Reeve, B. B. %A Hays, R. D. %A Bjorner, J. B. %A Cook, K. F. %A Crane, P. K. %A Teresi, J. A. %A Thissen, D. %A Revicki, D. A. %A Weiss, D. J. %A Hambleton, R. K. %A Liu, H. %A Gershon, R. C. %A Reise, S. P. %A Lai, J. S. %A Cella, D. %K *Health Status %K *Information Systems %K *Quality of Life %K *Self Disclosure %K Adolescent %K Adult %K Aged %K Calibration %K Databases as Topic %K Evaluation Studies as Topic %K Female %K Humans %K Male %K Middle Aged %K Outcome Assessment (Health Care)/*methods %K Psychometrics %K Questionnaires/standards %K United States %X BACKGROUND: The construction and evaluation of item banks to measure unidimensional constructs of health-related quality of life (HRQOL) is a fundamental objective of the Patient-Reported Outcomes Measurement Information System (PROMIS) project. OBJECTIVES: Item banks will be used as the foundation for developing short-form instruments and enabling computerized adaptive testing. The PROMIS Steering Committee selected 5 HRQOL domains for initial focus: physical functioning, fatigue, pain, emotional distress, and social role participation. This report provides an overview of the methods used in the PROMIS item analyses and proposed calibration of item banks. ANALYSES: Analyses include evaluation of data quality (eg, logic and range checking, spread of response distribution within an item), descriptive statistics (eg, frequencies, means), item response theory model assumptions (unidimensionality, local independence, monotonicity), model fit, differential item functioning, and item calibration for banking. RECOMMENDATIONS: Summarized are key analytic issues; recommendations are provided for future evaluations of item banks in HRQOL assessment. %B Medical Care %7 2007/04/20 %V 45 %P S22-31 %8 May %@ 0025-7079 (Print) %G eng %M 17443115 %0 Journal Article %J Medical Care %D 2000 %T Item response theory and health outcomes measurement in the 21st century %A Hays, R. D. %A Morales, L. S. %A Reise, S. P. %K *Models, Statistical %K Activities of Daily Living %K Data Interpretation, Statistical %K Health Services Research/*methods %K Health Surveys %K Human %K Mathematical Computing %K Outcome Assessment (Health Care)/*methods %K Research Design %K Support, Non-U.S. Gov't %K Support, U.S. Gov't, P.H.S. %K United States %X Item response theory (IRT) has a number of potential advantages over classical test theory in assessing self-reported health outcomes. IRT models yield invariant item and latent trait estimates (within a linear transformation), standard errors conditional on trait level, and trait estimates anchored to item content. IRT also facilitates evaluation of differential item functioning, inclusion of items with different response formats in the same scale, and assessment of person fit and is ideally suited for implementing computer adaptive testing. Finally, IRT methods can be helpful in developing better health outcome measures and in assessing change over time. These issues are reviewed, along with a discussion of some of the methodological and practical challenges in applying IRT methods. %B Medical Care %V 38 %P II28-II42 %G eng %M 10982088