%0 Journal Article %J Quality of Life Research %D 2009 %T Measuring global physical health in children with cerebral palsy: Illustration of a multidimensional bi-factor model and computerized adaptive testing %A Haley, S. M. %A Ni, P. %A Dumas, H. M. %A Fragala-Pinkham, M. A. %A Hambleton, R. K. %A Montpetit, K. %A Bilodeau, N. %A Gorton, G. E. %A Watson, K. %A Tucker, C. A. %K *Computer Simulation %K *Health Status %K *Models, Statistical %K Adaptation, Psychological %K Adolescent %K Cerebral Palsy/*physiopathology %K Child %K Child, Preschool %K Factor Analysis, Statistical %K Female %K Humans %K Male %K Massachusetts %K Pennsylvania %K Questionnaires %K Young Adult %X PURPOSE: The purposes of this study were to apply a bi-factor model for the determination of test dimensionality and a multidimensional CAT using computer simulations of real data for the assessment of a new global physical health measure for children with cerebral palsy (CP). METHODS: Parent respondents of 306 children with cerebral palsy were recruited from four pediatric rehabilitation hospitals and outpatient clinics. We compared confirmatory factor analysis results across four models: (1) one-factor unidimensional; (2) two-factor multidimensional (MIRT); (3) bi-factor MIRT with fixed slopes; and (4) bi-factor MIRT with varied slopes. We tested whether the general and content (fatigue and pain) person score estimates could discriminate across severity and types of CP, and whether score estimates from a simulated CAT were similar to estimates based on the total item bank, and whether they correlated as expected with external measures. RESULTS: Confirmatory factor analysis suggested separate pain and fatigue sub-factors; all 37 items were retained in the analyses. From the bi-factor MIRT model with fixed slopes, the full item bank scores discriminated across levels of severity and types of CP, and compared favorably to external instruments. CAT scores based on 10- and 15-item versions accurately captured the global physical health scores. CONCLUSIONS: The bi-factor MIRT CAT application, especially the 10- and 15-item versions, yielded accurate global physical health scores that discriminated across known severity groups and types of CP, and correlated as expected with concurrent measures. The CATs have potential for collecting complex data on the physical health of children with CP in an efficient manner. %B Quality of Life Research %7 2009/02/18 %V 18 %P 359-370 %8 Apr %@ 0962-9343 (Print)0962-9343 (Linking) %G eng %M 19221892 %2 2692519 %0 Journal Article %J Quality of Life Research %D 2007 %T Developing tailored instruments: item banking and computerized adaptive assessment %A Bjorner, J. B. %A Chang, C-H. %A Thissen, D. %A Reeve, B. B. %K *Health Status %K *Health Status Indicators %K *Mental Health %K *Outcome Assessment (Health Care) %K *Quality of Life %K *Questionnaires %K *Software %K Algorithms %K Factor Analysis, Statistical %K Humans %K Models, Statistical %K Psychometrics %X Item banks and Computerized Adaptive Testing (CAT) have the potential to greatly improve the assessment of health outcomes. This review describes the unique features of item banks and CAT and discusses how to develop item banks. In CAT, a computer selects the items from an item bank that are most relevant for and informative about the particular respondent; thus optimizing test relevance and precision. Item response theory (IRT) provides the foundation for selecting the items that are most informative for the particular respondent and for scoring responses on a common metric. The development of an item bank is a multi-stage process that requires a clear definition of the construct to be measured, good items, a careful psychometric analysis of the items, and a clear specification of the final CAT. The psychometric analysis needs to evaluate the assumptions of the IRT model such as unidimensionality and local independence; that the items function the same way in different subgroups of the population; and that there is an adequate fit between the data and the chosen item response models. Also, interpretation guidelines need to be established to help the clinical application of the assessment. Although medical research can draw upon expertise from educational testing in the development of item banks and CAT, the medical field also encounters unique opportunities and challenges. %B Quality of Life Research %7 2007/05/29 %V 16 %P 95-108 %@ 0962-9343 (Print) %G eng %M 17530450 %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 Quality of Life Research %D 2007 %T Patient-reported outcomes measurement and management with innovative methodologies and technologies %A Chang, C-H. %K *Health Status %K *Outcome Assessment (Health Care) %K *Quality of Life %K *Software %K Computer Systems/*trends %K Health Insurance Portability and Accountability Act %K Humans %K Patient Satisfaction %K Questionnaires %K United States %X Successful integration of modern psychometrics and advanced informatics in patient-reported outcomes (PRO) measurement and management can potentially maximize the value of health outcomes research and optimize the delivery of quality patient care. Unlike the traditional labor-intensive paper-and-pencil data collection method, item response theory-based computerized adaptive testing methodologies coupled with novel technologies provide an integrated environment to collect, analyze and present ready-to-use PRO data for informed and shared decision-making. This article describes the needs, challenges and solutions for accurate, efficient and cost-effective PRO data acquisition and dissemination means in order to provide critical and timely PRO information necessary to actively support and enhance routine patient care in busy clinical settings. %B Quality of Life Research %7 2007/05/29 %V 16 Suppl 1 %P 157-66 %@ 0962-9343 (Print)0962-9343 (Linking) %G eng %M 17530448 %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 Quality of Life Research %D 1997 %T Health status assessment for the twenty-first century: item response theory, item banking and computer adaptive testing %A Revicki, D. A. %A Cella, D. F. %K *Health Status %K *HIV Infections/diagnosis %K *Quality of Life %K Diagnosis, Computer-Assisted %K Disease Progression %K Humans %K Psychometrics/*methods %X Health status assessment is frequently used to evaluate the combined impact of human immunodeficiency virus (HIV) disease and its treatment on functioning and well-being from the patient's perspective. No single health status measure can efficiently cover the range of problems in functioning and well-being experienced across HIV disease stages. Item response theory (IRT), item banking and computer adaptive testing (CAT) provide a solution to measuring health-related quality of life (HRQoL) across different stages of HIV disease. IRT allows us to examine the response characteristics of individual items and the relationship between responses to individual items and the responses to each other item in a domain. With information on the response characteristics of a large number of items covering a HRQoL domain (e.g. physical function, and psychological well-being), and information on the interrelationships between all pairs of these items and the total scale, we can construct more efficient scales. Item banks consist of large sets of questions representing various levels of a HRQoL domain that can be used to develop brief, efficient scales for measuring the domain. CAT is the application of IRT and item banks to the tailored assessment of HRQoL domains specific to individual patients. Given the results of IRT analyses and computer-assisted test administration, more efficient and brief scales can be used to measure multiple domains of HRQoL for clinical trials and longitudinal observational studies. %B Quality of Life Research %7 1997/08/01 %V 6 %P 595-600 %8 Aug %@ 0962-9343 (Print) %G eng %M 9330558