Neuro-QOL: quality of life item banks for adults with neurological disorders: item development and calibrations based upon clinical and general population testing
Neuro-QOL provides a clinically relevant and psychometrically robust health-related quality of life (HRQL) assessment tool for both adults and children with common neurological disorders. We now report the psychometric results for the adult tools.
An extensive research, survey and consensus process was used to produce a list of 5 priority adult neurological conditions (stroke, multiple sclerosis, Parkinson’s disease, epilepsy and ALS). We identified relevant health related quality of life (HRQL) domains through multiple methods and data sources including a comprehensive review of the literature and literature search, expert interviews and surveys and patient and caregiver focus groups. The final domain framework consisted of 17 domains of Physical, Mental and Social health. There were five phases of item development: (1) identification of 3,482 extant items, (2) item classification and selection, (3) item review and revision, (4) cognitive interviews with 63 patients to assess their understanding of individual items and (5) field testing of 432 representative items.
Participants and Procedures
Participants were drawn from the US general population and clinical settings, and included both English and Spanish speaking subjects (N = 3,246). Confirmatory factor analysis (CFA) was used to evaluate the dimensionality of unidimensional domains. Where the domain structure was previously unknown, the dataset was split and first analyzed with exploratory factor analysis and then CFA. Samejima’s graded response model (GRM) was used to calculate IRT parameters. We further evaluated differential item functioning (DIF) on gender, education and age.
Thirteen unidimensional calibrated item banks consisting of 297 items were developed. All of the tested item banks had high reliability and few or no locally dependent items. The range of item slopes and thresholds with good information are reported for each of the item banks. The banks can support CAT and the development of short forms.
The Neuro-QOL measurement system provides item banks and short forms that enable PRO measurement in neurological research, minimizes patient burden and can be used to create multiple instrument types minimizing standard error. The 17 adult measures include 13 calibrated item banks, 3 item pools available for calibration work by others, and 1 stand-alone scale (index). The Neuro-QOL instruments provide a “common metric” of representative concepts for use across patient groups in different studies.
KeywordsOutcome measures Quality of life Neurological disorders Computerized adaptive testing, item banking
- 2.Perez, L., Huang, J., Jansky, L., Nowinski, C., Victorson, D., Peterman, A., et al. (2007). Using focus groups to inform the Neuro-QOL measurement tool: Exploring patient-centered, health-related quality of life concepts across neurological conditions. Journal of Neuroscience Nursing, 39(6), 342–353.PubMedCrossRefGoogle Scholar
- 3.Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: SAGE Publications.Google Scholar
- 5.Cella, D., Yount, S., Rothrock, N., Gershon, R., Cook, K., Reeve, B., Ader, D., Fries, J. F., Bruce, B., & Rose, M. (2007). The patient-reported outcomes measurement information system (PROMIS): Progress of an NIH roadmap cooperative group during its first two years. Medical Care, 45(5 Suppl 1), S3–S11.Google Scholar
- 6.DeWalt, D. A., Rothrock, N., Yount, S., Stone, A. A., & PROMIS Cooperative Group. (2007). Evaluation of item candidates: The PROMIS qualitative item review. Medical Care, 45(5 Suppl 1), S12–S21.Google Scholar
- 7.Reeve, B. B., Hays, R. D., Bjorner, J. B., Cook, K. F., Crane, P. K., Teresi, J. A., Thissen, D., Revicki, D. A., Weiss, D. J., Hambleton, R. K., Liu, H., Gershon, R., Reise, S. P., Lai, J. S., & Cella, D. (2007). Psychometric evaluation and calibration of health-related quality of life item banks: Plans for the patient-reported outcomes measurement information system (PROMIS). Medical Care, 45(5 Suppl 1), S22–S31.Google Scholar
- 9.Cella, D., Rothrock, N., Choi, S., Lai, J. S., Yount, S., & Gershon, R. (2010). PROMIS Overview: Development of new tools for measuring health-related quality of life and related outcomes in patients with chronic diseases. Annals of Behavioral Medicine, 39(Suppl 1–meeting abstract), 47.Google Scholar
- 12.Samejima, F., van der Liden, W. J., & Hambleton, R. (1996). The graded response model. In W. J. Van der Linden & R. K. Hambleton (Eds.), Handbook of modern item response theory (pp. 85–100). New York: Springer.Google Scholar
- 13.Thissen, D. (2003). MULTILOG (Version Windows (7.0)). Lincolnwood, IL: Scientific Software International.Google Scholar
- 20.Cella, D., Lai, J. S., Nowinski, C., Victorson, D., Peterman, A., Miller, D., Bethoux, F., Heinemann, A., Rubin, S., Cavasos, J., Reder, A., Sufit, R., Simuni, T., Holmes, G., Siderowf, A., Wojna, V., Bode, R., McKinney, N., Podrabsky, T., Wortman, K., Choi, S., Gershon, R., Rothrock, N., & Moy, C. (Submitted). Neuro-QOL: Brief measures of health-related quality of life for clinical research in neurology.Google Scholar