Advertisement

Quality of Life Research

, Volume 16, Supplement 1, pp 133–141 | Cite as

The future of outcomes measurement: item banking, tailored short-forms, and computerized adaptive assessment

  • David Cella
  • Richard Gershon
  • Jin-Shei Lai
  • Seung Choi
Original Paper

Abstract

The use of item banks and computerized adaptive testing (CAT) begins with clear definitions of important outcomes, and references those definitions to specific questions gathered into large and well-studied pools, or “banks” of items. Items can be selected from the bank to form customized short scales, or can be administered in a sequence and length determined by a computer programmed for precision and clinical relevance. Although far from perfect, such item banks can form a common definition and understanding of human symptoms and functional problems such as fatigue, pain, depression, mobility, social function, sensory function, and many other health concepts that we can only measure by asking people directly. The support of the National Institutes of Health (NIH), as witnessed by its cooperative agreement with measurement experts through the NIH Roadmap Initiative known as PROMIS (www.nihpromis.org), is a big step in that direction. Our approach to item banking and CAT is practical; as focused on application as it is on science or theory. From a practical perspective, we frequently must decide whether to re-write and retest an item, add more items to fill gaps (often at the ceiling of the measure), re-test a bank after some modifications, or split up a bank into units that are more unidimensional, yet less clinically relevant or complete. These decisions are not easy, and yet they are rarely unforgiving. We encourage people to build practical tools that are capable of producing multiple short form measures and CAT administrations from common banks, and to further our understanding of these banks with various clinical populations and ages, so that with time the scores that emerge from these many activities begin to have not only a common metric and range, but a shared meaning and understanding across users. In this paper, we provide an overview of item banking and CAT, discuss our approach to item banking and its byproducts, describe testing options, discuss an example of CAT for fatigue, and discuss models for long term sustainability of an entity such as PROMIS. Some barriers to success include limitations in the methods themselves, controversies and disagreements across approaches, and end-user reluctance to move away from the familiar.

Keywords

PROMIS Item response theory Item banks Computerized adaptive testing Health-related quality of life 

Reference List

  1. 1.
    Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Mahwah, N.J.: Lawrence Erlbaum Associates.Google Scholar
  2. 2.
    Lai, J. S., Cella, D., Peterman, A. et al. (2005). Anorexia/cachexia related quality of life for children with cancer: Testing the psychometric properties of the Pediatric Functional Assessment of Anorexia/Cachexia Therapy (peds-FAACT). Cancer, 104(7), 1531–1539.PubMedCrossRefGoogle Scholar
  3. 3.
    Gershon, R., Cella, D., Dineen, K. et al. (2003). Item response theory and health-related quality of life in cancer. Expert Review of Pharmacoeconomics and Outcomes Research, 3(6), 783–791.CrossRefGoogle Scholar
  4. 4.
    Gershon, R. (2005). Computerized adaptive testing. Journal of Applied Measurements, 6(1), 109–127.Google Scholar
  5. 5.
    Bunderson, V. C., Inouye, D. K., & Olsen, J. B. (1986). The four generations of computerized educational measurement. In R. L. Linn (Ed.), Educational measurement (pp. 367–407). New York: Macmillan Publishing.Google Scholar
  6. 6.
    Lautenschlager, G. J., & Flaherty, V. L. (1990). Computer administration of questions: More desirable or more social desirability? Journal of Applied Psychology, 75(3), 310–314.CrossRefGoogle Scholar
  7. 7.
    Reckase, M. D. (1989). Adaptive testing: The evolution of a good idea. Measurement Issues and Practice, 8(3), 11–15.CrossRefGoogle Scholar
  8. 8.
    McHorney, C. A., & Cohen, A. S. (2000). Equating health status measures with Item Response Theory: Illustrations with functional status items. Medical Care, 38(Suppl 9), II43–II59.PubMedGoogle Scholar
  9. 9.
    Ware, J. E., Bjorner, J. B., & Kosinski, M. (2000). Practical implications of Item Response Theory and computerized adaptive testing: A brief summary of ongoing studies of widely used headache impact scales. Medical Care, 38(Suppl 9), 473–483.Google Scholar
  10. 10.
    Cella, D., & Chang, C. H. (2000). A discussion of Item Response Theory (IRT) and its applications in health status assessment. Medical Care, 38(Suppl 9), II66–II72.PubMedGoogle Scholar
  11. 11.
    Wolfe, F., & Pincus, T. (1999). Listening to the patient: A practical guide to self-report questionnaires in clinical care. Arthritis Rheumatism, 42(9), 1797–1808.PubMedCrossRefGoogle Scholar
  12. 12.
    Detmar, S. B., Muller, M. J., Schornagel, J. H. et al. (2002). Health-related quality-of-life assessments and patient-physician communication: A randomized controlled trial. JAMA, 288(23), 3027–3034.PubMedCrossRefGoogle Scholar
  13. 13.
    Velikova, G., Brown, J., Booth, L., Smith, A., Bown, P., Lynch, P., & Selby, P. (2003). A randomized study of quality of life measurements in oncology practice - effects on patient well-being and process of care. Proceedings of the American society of clinical oncology, 22, 728.Google Scholar
  14. 14.
    Jacobsen, P. B., Davis, K., & Cella, D. (2002). Assessing quality of life in research and clinical practice. Oncology, 16(Suppl 9), 133–139.PubMedGoogle Scholar
  15. 15.
    Bode, R. K., Cella, D., Lai, J. -S. et al. (2003). Developing an initial physical function item bank from existing sources. Journal of Applied Measurements, 4(2), 124–136.Google Scholar
  16. 16.
    Bode, R., Lai, J. -S., Heinemann, A. et al. (2006). Expansion of a physical function item bank and development of an abbreviated form for clinical research. Journal of Applied Measurements, 7(1), 1–15.Google Scholar
  17. 17.
    Lai, J. -S., Cella, D., Chang, C. -H. et al. (2003). Item banking to improve, shorten and computerize self-reported fatigue: An illustration of steps to create a core item bank from the FACIT-Fatigue Scale. Quality of Life Research, 12, 485–501.PubMedCrossRefGoogle Scholar
  18. 18.
    Lai, J. -S., Cella, D., Dineen, K. et al. (2005). An item bank was created to improve the measurement of cancer-related fatigue. Journal of Clinical Epidemiology, 58(2), 190–197.PubMedCrossRefGoogle Scholar
  19. 19.
    Lai, J. -S., Dineen, K., Reeve, B. et al. (2005). An item response theory based pain item bank can enhance measurement precision. Journal of Pain and Symptomology Management, 30(3), 278–288.CrossRefGoogle Scholar
  20. 20.
    MSP for Windows (Version 5) [computer program]. (2000).Google Scholar
  21. 21.
    Bode, R. K., Lai, J. -S., Cella, D. et al. (2003). Issues in the development of an item bank. Archives of Physical Medicine and Rehabiitation, 84(4 Suppl 2), S52–S60.CrossRefGoogle Scholar
  22. 22.
    Carlson, L. E., Speca, M., Hagen, N. et al. (2001). Computerized quality-of-life screening in a cancer pain clinic. Journal of Palliative Care, 17(1), 46–52.PubMedGoogle Scholar
  23. 23.
    Chang, C. H., Cella, D., Fernandez, O. et al. (2002). Quality of life in multiple sclerosis patients in Spain. Multiple Sclerosis, 8(6), 527–531.PubMedCrossRefGoogle Scholar
  24. 24.
    Detmar, S. B., & Aaronson, N. K. (1998). Quality of life assessment in daily clinical oncology practice: A feasibility study. European Journal of Cancer, 34(8), 1181–1186.PubMedCrossRefGoogle Scholar
  25. 25.
    Taenzer, P., Bultz, B. D., Carlson, L. E. et al. (2000). Impact of computerized quality of life screening on physician behavior and patient satisfaction in lung cancer outpatients. Psycho-Oncology, 9(3), 203–213.PubMedCrossRefGoogle Scholar
  26. 26.
    Velikova, G., Wright, P., Smith, A. B. et al. (2001) Self-reported quality of life of individual cancer patients: Concordance of results with disease course and medical records. Journal of Clinical Oncology, 19(7), 2064–2073.PubMedGoogle Scholar
  27. 27.
    Velikova, G., Brown, J. M., Smith, A. B. et al. (2002) Computer-based quality of life questionnaires may contribute to doctor-patient interactions in oncology. British Journal of Cancer, 86(1), 51–59.PubMedCrossRefGoogle Scholar
  28. 28.
    Velikova G., Booth L., Smith A. B. et al. (2004). Measuring quality of life in routine oncology practice improves communication and patient well-being: A randomized controlled trial. Journal of Clinical Oncology, 22(4), 714–724.PubMedCrossRefGoogle Scholar
  29. 29.
    Yount, S., Davis, K., Cella, D. et al. (2003). Brief patient symptom assessment: Research and clinical applications. To be presented at the 10th World Conference on Lung Cancer.Google Scholar
  30. 30.
    Hahn, E. A., Cella, D., Dobrez, D. G. et al. (2003). Quality of life assessment for low literacy Latinos: A new multimedia program for self-administration. Journal of Oncology Management, 12(5), 9–12.PubMedGoogle Scholar
  31. 31.
    Higginson, I. J., & Carr, A. J. (2001). Measuring quality of life: Using quality of life measures in the clinical setting. BMJ, 322(7297), 1297–1300.PubMedCrossRefGoogle Scholar
  32. 32.
    Buxton, J., White, M., & Osoba, D. (1998). Patients’ experiences using a computerized program with a touch-sensitive video monitor for the assessment of health-related quality of life. Quality of Life Research, 7(6), 513–519.PubMedCrossRefGoogle Scholar
  33. 33.
    Davis, K. M., Chang, C. -H., Lai, J. -S., & Cella, D. (2002). Feasibility and acceptability of computerized adaptive testing (CAT) for fatigue monitoring in clinical practice. Quality of Life Research, 11(7), 134.Google Scholar
  34. 34.
    Bergstrom, B. A., & Lunz, M. E. (1999). CAT for certification and licensure. In F. Drasgow & J. B. Olson-Buchanan (Eds.), Innovations in computerized assessment (pp. 67–92). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.Google Scholar
  35. 35.
    Gershon, R. C. (2004) The ABCs of Computerized Adaptive Testing. In T. M. Wood & W. Zhi (Eds.), Measurement issues and practice in physical activity. Champaign, IL: Human kinetics.Google Scholar
  36. 36.
    Ware, J. E. Jr., Kosinski, M., Bjorner, J. B. et al. (2003) Applications of computerized adaptive testing (CAT) to the assessment of headache impact. Quality of Life Research, 12(8), 935–952.PubMedCrossRefGoogle Scholar
  37. 37.
    Bergstrom, B., & Cline, A. (2003). Beyond multiple choice: Innovations in professional testing. CLEAR Exam Review. Summer.Google Scholar
  38. 38.
    van der Linden, W. J., & Pashley, P. J. (2000) Item selection and ability estimation in adaptive testing. In W. J. van der Linden & C. A. W. Glas (Eds.), Computerized adaptive testing: theory and practice. Norwell, MA: Kluwer.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  • David Cella
    • 1
    • 2
  • Richard Gershon
    • 3
    • 4
  • Jin-Shei Lai
    • 2
    • 5
  • Seung Choi
    • 2
  1. 1.Psychiatry and Behavioral Sciences, Institute for Healthcare Studies, Feinberg School of MedicineNorthwestern UniversityEvanstonUSA
  2. 2.Center on Outcomes, Research and Education (CORE)Evanston Northwestern HealthcareEvanstonUSA
  3. 3.Psychometrics and Informatics, Center on Outcomes, Research and Education (CORE)Evanston Northwestern HealthcareEvanstonUSA
  4. 4.Department of PsychologyNorthwestern UniversityEvanstonUSA
  5. 5.Department of Pediatrics, Institute for Healthcare Studies, Feinberg School of MedicineNorthwestern UniversityEvanstonUSA

Personalised recommendations