Quality of Life Research

, Volume 21, Issue 3, pp 475–486 | Cite as

Neuro-QOL: quality of life item banks for adults with neurological disorders: item development and calibrations based upon clinical and general population testing

  • Richard C. GershonEmail author
  • Jin Shei Lai
  • Rita Bode
  • Seung Choi
  • Claudia Moy
  • Tom Bleck
  • Deborah Miller
  • Amy Peterman
  • David Cella



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.


Outcome measures Quality of life Neurological disorders Computerized adaptive testing, item banking 

Supplementary material

11136_2011_9958_MOESM1_ESM.docx (30 kb)
Supplementary material (DOCX 31 kb)


  1. 1.
    Lynch, E. B., Butt, Z., Heinemann, A., Victorson, D., Nowinski, C., Perez, L., et al. (2008). A qualitative study of quality of life after stroke: The importance of social relationships. Journal of Rehabilitation Medicine, 40, 518–523.PubMedCrossRefGoogle Scholar
  2. 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. 3.
    Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Newbury Park, CA: SAGE Publications.Google Scholar
  4. 4.
    Gershon, R., Cella, D., Dineen, K., Rosenbloom, S., Peterman, A., & Lai, J. S. (2003). Item response theory and health-related quality of life in cancer. Expert Review of Pharmacoeconomics & Outcomes Research, 3(6), 783–791.CrossRefGoogle Scholar
  5. 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. 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. 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
  8. 8.
    Cella, D., Gershon, R., Lai, J. S., & Choi, S. (2007). The future of outcomes measurement: Item banking, tailored short-forms, and computerized adaptive assessment. Quality of Life Research, 16(Suppl 1), 133–141.PubMedCrossRefGoogle Scholar
  9. 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
  10. 10.
    Haley, S. M., Coster, W. J., Andres, P. L., Ludlow, L. H., Ni, P., Bond, T. L., et al. (2004). Activity outcome measurement for postacute care. Medical Care, 42(1 Suppl), I49–I61.PubMedGoogle Scholar
  11. 11.
    Eremenco, S. L., Cella, D., & Arnold, B. J. (2005). A comprehensive method for the translation and cross-cultural validation of health status questionnaires. Evaluation and the Health Professions, 28(2), 212–232.CrossRefGoogle Scholar
  12. 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. 13.
    Thissen, D. (2003). MULTILOG (Version Windows (7.0)). Lincolnwood, IL: Scientific Software International.Google Scholar
  14. 14.
    Budescu, D. V., Cohen, Y., & Ben Simon, A. (1997). A revised modified parallel analysis for the construction of unidimensional item pools. Applied Psychological Measurement, 21(3), 233–252.CrossRefGoogle Scholar
  15. 15.
    Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous item response theory models. Applied Psychological Measurement, 24, 50–64.CrossRefGoogle Scholar
  16. 16.
    Orlando, M., & Thissen, D. (2003). Further examination of the performance of S-X2, an item fit index for dichotomous item response theory models. Applied Psychological Measurement, 27, 289–298.CrossRefGoogle Scholar
  17. 17.
    O’Connor, B. P. (2000). SPSS and SAS programs for determining the number of components using parallel analysis and Velicer’s MAP test. Behavior Research Methods, Instruments & Computers, 32(3), 396–402.CrossRefGoogle Scholar
  18. 18.
    Gibbons, R., & Hedeker, D. (1992). Full-information item bi-factor analysis. Psychometrika, 57(3), 423–436.CrossRefGoogle Scholar
  19. 19.
    Gershon, R., Rothrock, N. E., Hanrahan, R. T., Jansky, L. J., Harniss, M., & Riley, W. (2010). The development of a clinical outcomes survey research application: Assessment CenterSM. Quality of Life Research, 19(5), 677–685.PubMedCrossRefGoogle Scholar
  20. 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
  21. 21.
    Choi, S. W., Reise, S. P., Pilkonis, P. A., Hays, R. D., & Cella, D. (2009). Efficiency of static and computer adaptive short forms compared to full length measures of depressive symptoms. Quality of Life Research, 19(1), 125–136.PubMedCrossRefGoogle Scholar
  22. 22.
    Choi, S. W., & Swartz, R. J. (2009). Comparison of CAT item selection criteria for polytomous items. Applied Psychological Measurement, 33(6), 419–440.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Richard C. Gershon
    • 1
    Email author
  • Jin Shei Lai
    • 1
  • Rita Bode
    • 1
  • Seung Choi
    • 1
  • Claudia Moy
    • 2
  • Tom Bleck
    • 3
  • Deborah Miller
    • 4
  • Amy Peterman
    • 5
  • David Cella
    • 1
  1. 1.Northwestern UniversityChicagoUSA
  2. 2.National Institute of Neurological Disorders and StrokeBethesdaUSA
  3. 3.Rush University Medical CenterChicagoUSA
  4. 4.Cleveland ClinicClevelandUSA
  5. 5.University of North Carolina at CharlotteCharlotteUSA

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