Testing item response theory invariance of the standardized Quality-of-life Disease Impact Scale (QDIS®) in acute coronary syndrome patients: differential functioning of items and test
- 336 Downloads
The Quality-of-life (QOL) Disease Impact Scale (QDIS®) standardizes the content and scoring of QOL impact attributed to different diseases using item response theory (IRT). This study examined the IRT invariance of the QDIS-standardized IRT parameters in an independent sample.
The differential functioning of items and test (DFIT) of a static short-form (QDIS-7) was examined across two independent sources: patients hospitalized for acute coronary syndrome (ACS) in the TRACE-CORE study (N = 1,544) and chronically ill US adults in the QDIS standardization sample. “ACS-specific” IRT item parameters were calibrated and linearly transformed to compare to “standardized” IRT item parameters. Differences in IRT model-expected item, scale and theta scores were examined. The DFIT results were also compared in a standard logistic regression differential item functioning analysis.
Item parameters estimated in the ACS sample showed lower discrimination parameters than the standardized discrimination parameters, but only small differences were found for thresholds parameters. In DFIT, results on the non-compensatory differential item functioning index (range 0.005–0.074) were all below the threshold of 0.096. Item differences were further canceled out at the scale level. IRT-based theta scores for ACS patients using standardized and ACS-specific item parameters were highly correlated (r = 0.995, root-mean-square difference = 0.09). Using standardized item parameters, ACS patients scored one-half standard deviation higher (indicating greater QOL impact) compared to chronically ill adults in the standardization sample.
The study showed sufficient IRT invariance to warrant the use of standardized IRT scoring of QDIS-7 for studies comparing the QOL impact attributed to acute coronary disease and other chronic conditions.
KeywordsItem response theory invariance Differential item functioning Differential test (scale) functioning Measurement invariance Disease-specific quality-of-life measures
Acute coronary syndrome
ACS-specific linearly transformed
Computerized adaptive testing
Compensatory differential item functioning
Confirmatory factor analysis
Differential functioning of items and tests
The computerized adaptive Assessment of disease impact project
Differential item functioning
Differential test (scale) functioning
Generalized partial credit model
Item characteristic curve
Item parameter drift
Item response theory
Minnesota Living with Heart Failure Questionnaire
Non-compensatory differential item functioning
Patient Reported Outcomes Measurement Information System
Quality-of-life Disease Impact Scale
7-item short-form of QDIS®
Seattle Angina Questionnaire
Test characteristic curve
The Transitions, Risks, and Actions in Coronary Events-Center for Outcomes Research and Education project
- 4.Rector, T. S., Kubo, S. H., & Cohn, J. N. (1987). Patients’ self-assessment of their congestive heart failure. Part 2: content, reliability and validity of a new measure, the Minnesota Living with Heart Failure questionnaire. Heart Failure, 3, 198–209.Google Scholar
- 6.Ware, J. E, Jr, Harrington, M., Guyer, R., & Boulanger, R. (2012). A system for integrating generic and disease-specific patient-reported outcome (PRO) measures. Patient Reported Outcomes Newsletter, 48(Fall), 2–4.Google Scholar
- 7.Ware, J. E, Jr, Gandek, B., & Guyer, R. (2014). Measuring disease-specific quality of life (QOL) impact: A manual for users of the QOL Disease Impact Scale (QDIS ® ). Worcester, MA: JWRG Incorporated.Google Scholar
- 8.Ware, J. E. Jr., Guyer, R., Gandek, B., Deng, N. Standardizing disease-specific quality of life (QOL) impact measures: Development and initial evaluation of the QOL Disease Impact Scale (QDIS ® ) (submitted).Google Scholar
- 9.Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: Sage.Google Scholar
- 11.Raju, N. S., van der Linden, W. J., & Fleer, P. F. (1995). IRT-based internal measures of differential functioning of items and tests. Applied Psychological Measurement, 19, 353–368.Google Scholar
- 12.Oldroyd, J. C., Cyril, S., Wijayatilaka, B. S., et al. (2013). Evaluating the impact of depression, anxiety & autonomic function on health related quality of life, vocational functioning and health care utilisation in acute coronary syndrome patients: the ADVENT study protocol. BMC Cardiovascular Disorders, 13, 103.PubMedCentralPubMedCrossRefGoogle Scholar
- 20.Swaminathan, H., Hambleton, R. K., & Rogers, H. J. (2007). Assessing the fit of item response theory models. In C. R. Rao & S. Sinharay (Eds.), Handbook of Statistics: Psychometrics (pp. 683–718). London: Elsevier Publishing Co.Google Scholar
- 21.Hambleton, R. K., & Han, N. (2005). Assessing the fit of IRT models to educational and psychological test data: A five step plan and several graphical displays. In W. R. Lenderking & D. Revicki (Eds.), Advances in health outcomes research methods, measurement, statistical analysis, and clinical applications (pp. 57–78). Washington: Degnon Associates.Google Scholar
- 22.Muraki, E., & Bock, R. D. (2003). PARSCALE 4: IRT item analysis and test scoring for rating scale data [computer program]. Chicago, IL: Scientific Software.Google Scholar
- 24.De Ayala, R. J. (2009). The theory and practice of item response theory. New York: The Guilford Press.Google Scholar
- 27.Sukin, Tia M. (2010). Item parameter drift as an indication of differential opportunity to learn: An exploration of item flagging methods & accurate classification of examinees. Doctoral dissertation. http://scholarworks.umass.edu/open_access_dissertations/301 Accessed 13 March 2014.
- 29.Fleer, P. F. (1993). A Monte Carlo assessment of a new measure of item and test bias. Doctoral dissertation, Illinois Institute of Technology. Dissertation Abstracts International, 54, 2266.Google Scholar
- 30.Raju, N. (2000). Notes accompanying the differential functioning of items and tests (DFIT) computer program. Chicago: Illinois Institute of Technology.Google Scholar
- 31.Teresi, J. A., Ocepek-Welikson, K., Kleinman, M., et al. (2007). Evaluating measurement equivalence using the item response theory log-likelihood ratio (IRTLR) method to assess differential item functioning (DIF): Applications (with illustrations) to measures of physical functioning ability and general distress. Quality of Life Research, 16, 43–68.PubMedCrossRefGoogle Scholar
- 33.Zumbo, B. D. (1999). A handbook on the theory and methods of differential item functioning (DIF): Logistic regression modeling as a unitary framework for binary and Likert-type (Ordinal) item scores. Ottawa ON: Directorate of Human Resources Research and Evaluation, Department of National Defense.Google Scholar
- 35.Reeve, B., Hays, R. D., Bjorner, J., et al., on behalf of the PROMIS cooperative group. (2007). Psychometric evaluation and calibration of health–related quality of life item banks: Plans for the Patient-Reported Outcome Measurement Information System (PROMIS). Medical Care, 45(5), S22–S31.Google Scholar
- 36.Rose, M., Bjorner, J. B., Becker, J., Fries, J. F., & Ware, J. E. (2008). Evaluation of a preliminary physical function item bank supports the expected advantages of the Patient-Reported Outcomes Measurement Information System (PROMIS). Journal of Clinical Epidemiology, 61, 17–33.PubMedCrossRefGoogle Scholar
- 44.Ware, J. E, Jr, Guyer, R., Harrington, M., & Boulanger, R. (2012). Evaluation of a more comprehensive survey item bank for standardizing disease-specific impact comparisons across chronic conditions. Quality of Life Research, 21(1 Suppl), 27–28.Google Scholar
- 45.R Core Team (2014). R: A language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. http://www.R-project.org/.
- 50.Bjorner, J. B., Rose, M., Gandek, B., Stone, A. A., Junghaenel, D. U., & Ware, J. E, Jr. (2013). Difference in method of administration did not significantly impact item response: An IRT-based analysis from the Patient-Reported Outcomes Measurement Information System initiative. Quality of Life Research, 23, 217–227.PubMedCentralPubMedCrossRefGoogle Scholar