@article {29,
	title = {Developing tailored instruments: item banking and computerized adaptive assessment},
	journal = {Quality of Life Research},
	volume = {16},
	number = {Suppl 1},
	year = {2007},
	note = {Bjorner, Jakob BueChang, Chih-HungThissen, DavidReeve, Bryce B1R43NS047763-01/NS/United States NINDSAG015815/AG/United States NIAResearch 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 1:95-108. Epub 2007 Feb 15.},
	pages = {95-108},
	edition = {2007/05/29},
	abstract = {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.},
	keywords = {*Health Status, *Health Status Indicators, *Mental Health, *Outcome Assessment (Health Care), *Quality of Life, *Questionnaires, *Software, Algorithms, Factor Analysis, Statistical, Humans, Models, Statistical, Psychometrics},
	isbn = {0962-9343 (Print)},
	author = {Bjorner, J. B. and Chang, C-H. and Thissen, D. and Reeve, B. B.}
}
@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 {53,
	title = {Patient-reported outcomes measurement and management with innovative methodologies and technologies},
	journal = {Quality of Life Research},
	volume = {16 Suppl 1},
	year = {2007},
	note = {Chang, Chih-HungR21CA113191/CA/NCI NIH HHS/United StatesResearch 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 1:157-66. Epub 2007 May 26.},
	pages = {157-66},
	edition = {2007/05/29},
	abstract = {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.},
	keywords = {*Health Status, *Outcome Assessment (Health Care), *Quality of Life, *Software, Computer Systems/*trends, Health Insurance Portability and Accountability Act, Humans, Patient Satisfaction, Questionnaires, United States},
	isbn = {0962-9343 (Print)0962-9343 (Linking)},
	author = {Chang, C-H.}
}
@article {48,
	title = {Advances in quality of life measurements in oncology patients},
	journal = {Seminars in Oncology},
	volume = {29},
	number = {3 Suppl 8},
	year = {2002},
	note = {0093-7754 (Print)Journal ArticleReview},
	month = {Jun},
	pages = {60-8},
	abstract = {Accurate assessment of the quality of life (QOL) of patients can provide important clinical information to physicians, especially in the area of oncology. Changes in QOL are important indicators of the impact of a new cytotoxic therapy, can affect a patient{\textquoteright}s willingness to continue treatment, and may aid in defining response in the absence of quantifiable endpoints such as tumor regression. Because QOL is becoming an increasingly important aspect in the management of patients with malignant disease, it is vital that the instruments used to measure QOL are reliable and accurate. Assessment of QOL involves a multidimensional approach that includes physical, functional, social, and emotional well-being, and the most comprehensive instruments measure at least three of these domains. Instruments to measure QOL can be generic (eg, the Nottingham Health Profile), targeted toward specific illnesses (eg, Functional Assessment of Cancer Therapy - Lung), or be a combination of generic and targeted. Two of the most widely used examples of the combination, or hybrid, instruments are the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 Items and the Functional Assessment of Chronic Illness Therapy. A consequence of the increasing international collaboration in clinical trials has been the growing necessity for instruments that are valid across languages and cultures. To assure the continuing reliability and validity of QOL instruments in this regard, item response theory can be applied. Techniques such as item response theory may be used in the future to construct QOL item banks containing large sets of validated questions that represent various levels of QOL domains. As QOL becomes increasingly important in understanding and approaching the overall management of cancer patients, the tools available to clinicians and researchers to assess QOL will continue to evolve. While the instruments currently available provide reliable and valid measurement, further improvements in precision and application are anticipated.},
	keywords = {*Quality of Life, *Sickness Impact Profile, Cross-Cultural Comparison, Culture, Humans, Language, Neoplasms/*physiopathology, Questionnaires},
	author = {Cella, D. and Chang, C-H. and Lai, J. S. and Webster, K.}
}
