%0 Journal Article %J Quality of Life Research %D 2007 %T IRT health outcomes data analysis project: an overview and summary %A Cook, K. F. %A Teal, C. R. %A Bjorner, J. B. %A Cella, D. %A Chang, C-H. %A Crane, P. K. %A Gibbons, L. E. %A Hays, R. D. %A McHorney, C. A. %A Ocepek-Welikson, K. %A Raczek, A. E. %A Teresi, J. A. %A Reeve, B. B. %K *Data Interpretation, Statistical %K *Health Status %K *Quality of Life %K *Questionnaires %K *Software %K Female %K HIV Infections/psychology %K Humans %K Male %K Neoplasms/psychology %K Outcome Assessment (Health Care)/*methods %K Psychometrics %K Stress, Psychological %X BACKGROUND: In June 2004, the National Cancer Institute and the Drug Information Association co-sponsored the conference, "Improving the Measurement of Health Outcomes through the Applications of Item Response Theory (IRT) Modeling: Exploration of Item Banks and Computer-Adaptive Assessment." A component of the conference was presentation of a psychometric and content analysis of a secondary dataset. OBJECTIVES: A thorough psychometric and content analysis was conducted of two primary domains within a cancer health-related quality of life (HRQOL) dataset. RESEARCH DESIGN: HRQOL scales were evaluated using factor analysis for categorical data, IRT modeling, and differential item functioning analyses. In addition, computerized adaptive administration of HRQOL item banks was simulated, and various IRT models were applied and compared. SUBJECTS: The original data were collected as part of the NCI-funded Quality of Life Evaluation in Oncology (Q-Score) Project. A total of 1,714 patients with cancer or HIV/AIDS were recruited from 5 clinical sites. MEASURES: Items from 4 HRQOL instruments were evaluated: Cancer Rehabilitation Evaluation System-Short Form, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, Functional Assessment of Cancer Therapy and Medical Outcomes Study Short-Form Health Survey. RESULTS AND CONCLUSIONS: Four lessons learned from the project are discussed: the importance of good developmental item banks, the ambiguity of model fit results, the limits of our knowledge regarding the practical implications of model misfit, and the importance in the measurement of HRQOL of construct definition. With respect to these lessons, areas for future research are suggested. The feasibility of developing item banks for broad definitions of health is discussed. %B Quality of Life Research %7 2007/03/14 %V 16 %P 121-132 %@ 0962-9343 (Print) %G eng %M 17351824 %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 2006 %T Overview of quantitative measurement methods. Equivalence, invariance, and differential item functioning in health applications %A Teresi, J. A. %K *Cross-Cultural Comparison %K Data Interpretation, Statistical %K Factor Analysis, Statistical %K Guidelines as Topic %K Humans %K Models, Statistical %K Psychometrics/*methods %K Statistics as Topic/*methods %K Statistics, Nonparametric %X BACKGROUND: Reviewed in this article are issues relating to the study of invariance and differential item functioning (DIF). The aim of factor analyses and DIF, in the context of invariance testing, is the examination of group differences in item response conditional on an estimate of disability. Discussed are parameters and statistics that are not invariant and cannot be compared validly in crosscultural studies with varying distributions of disability in contrast to those that can be compared (if the model assumptions are met) because they are produced by models such as linear and nonlinear regression. OBJECTIVES: The purpose of this overview is to provide an integrated approach to the quantitative methods used in this special issue to examine measurement equivalence. The methods include classical test theory (CTT), factor analytic, and parametric and nonparametric approaches to DIF detection. Also included in the quantitative section is a discussion of item banking and computerized adaptive testing (CAT). METHODS: Factorial invariance and the articles discussing this topic are introduced. A brief overview of the DIF methods presented in the quantitative section of the special issue is provided together with a discussion of ways in which DIF analyses and examination of invariance using factor models may be complementary. CONCLUSIONS: Although factor analytic and DIF detection methods share features, they provide unique information and can be viewed as complementary in informing about measurement equivalence. %B Medical Care %7 2006/10/25 %V 44 %P S39-49 %8 Nov %@ 0025-7079 (Print)0025-7079 (Linking) %G eng %M 17060834