@article {86,
	title = {IRT health outcomes data analysis project: an overview and summary},
	journal = {Quality of Life Research},
	volume = {16},
	number = {Suppl. 1},
	year = {2007},
	note = {Cook, Karon FTeal, Cayla RBjorner, Jakob BCella, DavidChang, Chih-HungCrane, Paul KGibbons, Laura EHays, Ron DMcHorney, Colleen AOcepek-Welikson, KatjaRaczek, Anastasia ETeresi, Jeanne AReeve, Bryce B1U01AR52171-01/AR/United States NIAMSR01 (CA60068)/CA/United States NCIY1-PC-3028-01/PC/United States NCIResearch Support, N.I.H., ExtramuralNetherlandsQuality of life research : an international journal of quality of life aspects of treatment, care and rehabilitationQual Life Res. 2007;16 Suppl 1:121-32. Epub 2007 Mar 10.},
	pages = {121-132},
	edition = {2007/03/14},
	abstract = {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.},
	keywords = {*Data Interpretation, Statistical, *Health Status, *Quality of Life, *Questionnaires, *Software, Female, HIV Infections/psychology, Humans, Male, Neoplasms/psychology, Outcome Assessment (Health Care)/*methods, Psychometrics, Stress, Psychological},
	isbn = {0962-9343 (Print)},
	author = {Cook, K. F. and Teal, C. R. and Bjorner, J. B. and Cella, D. and Chang, C-H. and Crane, P. K. and Gibbons, L. E. and Hays, R. D. and McHorney, C. A. and Ocepek-Welikson, K. and Raczek, A. E. and Teresi, J. A. and Reeve, B. B.}
}
@article {328,
	title = {Psychometric evaluation and calibration of health-related quality of life item banks: plans for the Patient-Reported Outcomes Measurement Information System (PROMIS)},
	journal = {Medical Care},
	volume = {45},
	number = {5 Suppl 1},
	year = {2007},
	note = {Reeve, Bryce BHays, Ron DBjorner, Jakob BCook, Karon FCrane, Paul KTeresi, Jeanne AThissen, DavidRevicki, Dennis AWeiss, David JHambleton, Ronald KLiu, HonghuGershon, RichardReise, Steven PLai, Jin-sheiCella, DavidPROMIS Cooperative GroupAG015815/AG/United States NIAResearch Support, N.I.H., ExtramuralUnited StatesMedical careMed Care. 2007 May;45(5 Suppl 1):S22-31.},
	month = {May},
	pages = {S22-31},
	edition = {2007/04/20},
	abstract = {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.},
	keywords = {*Health Status, *Information Systems, *Quality of Life, *Self Disclosure, Adolescent, Adult, Aged, Calibration, Databases as Topic, Evaluation Studies as Topic, Female, Humans, Male, Middle Aged, Outcome Assessment (Health Care)/*methods, Psychometrics, Questionnaires/standards, United States},
	isbn = {0025-7079 (Print)},
	author = {Reeve, B. B. and Hays, R. D. and Bjorner, J. B. and Cook, K. F. and Crane, P. K. and Teresi, J. A. and Thissen, D. and Revicki, D. A. and Weiss,  D. J. and Hambleton, R. K. and Liu, H. and Gershon, R. C. and Reise, S. P. and Lai, J. S. and Cella, D.}
}
@article {191,
	title = {Item response theory and health outcomes measurement in the 21st century},
	journal = {Medical Care},
	volume = {38},
	number = {9 Suppl II},
	year = {2000},
	note = {204349670025-7079Journal Article},
	pages = {II28-II42},
	abstract = {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.},
	keywords = {*Models, Statistical, Activities of Daily Living, Data Interpretation, Statistical, Health Services Research/*methods, Health Surveys, Human, Mathematical Computing, Outcome Assessment (Health Care)/*methods, Research Design, Support, Non-U.S. Gov{\textquoteright}t, Support, U.S. Gov{\textquoteright}t, P.H.S., United States},
	author = {Hays, R. D. and Morales, L. S. and Reise, S. P.}
}
