Journal of Clinical Epidemiology
Volume 59, Issue 3 , Pages 290-298 , March 2006

Simulated computerized adaptive test for patients with shoulder impairments was efficient and produced valid measures of function

  • Dennis L. Hart

      Affiliations

    • Focus On Therapeutic Outcomes, Inc., 551 Yopps Cove Road, White Stone, VA 22578, USA
    • Corresponding Author InformationCorresponding author.
  • ,
  • Karon F. Cook

      Affiliations

    • Veteran's Affairs Measurement Excellence, Training Research & Information Center (METRIC), U.S. Department of Veterans Affairs, Houston, TX, USA
    • Department of Rehabilitation Medicine, University of Washington, Seattle, WA, USA
  • ,
  • Jerome E. Mioduski

      Affiliations

    • Focus On Therapeutic Outcomes, Inc., Knoxville, TN, USA
  • ,
  • Cayla R. Teal

      Affiliations

    • Houston Center for Quality of Care & Utilization Studies, Baylor College of Medicine and Michael E. DeBakey Veterans Affairs Medical Center, Houston, TX, USA
    • Department of Medicine, Baylor College of Medicine, Houston, TX, USA
  • ,
  • Paul K. Crane

      Affiliations

    • Division of General Internal Medicine, University of Washington School of Medicine, Seattle, WA, USA

,Accepted 8 August 2005.

References 

  1. Hart DL, Mioduski JE, Stratford PW. Simulated computerized adaptive tests for measuring functional status were efficient with good discriminant validity in patients with hip, knee, or foot/ankle impairments. J Clin Epidemiol. 2005;58:629–638
  2. Hambleton RK. Emergence of item response modeling in instrument development and data analysis. Med Care. 2000;38(9 Suppl):II-60–5
  3. Ware JE, Bjorner JB, Kosinski M. Practical implications of item response theory and computerized adaptive testing: a brief summary of ongoing studies of widely used headache impact scales. Med Care. 2000;38(9 Suppl):II-73–82
  4. Ware JE, Kosinski M, Bjorner JB, Bayliss MS, Batenhorst A, Dahlof CG, et al. Applications of computerized adaptive testing (CAT) to the assessment of headache impact. Qual Life Res. 2003;12:935–952
  5. Dijkers MP. A computer adaptive testing simulation applied to the FIM instrument motor component. Arch Phys Med Rehabil. 2003;84:384–393
  6. Haley SM, Coster WJ, Andres PL, Kosinski M, Ni P. Score comparability of short forms and computerized adaptive testing: simulation study with the activity measure for post-acute care. Arch Phys Med Rehabil. 2004;85:661–666
  7. Haley S, Jette A. Extending the frontier of rehabilitation outcome measurement and research. J Outcome Meas. 2000;4:31–41
  8. Ware JE. Conceptualization and measurement of health-related quality of life: comments on an evolving field. Arch Phys Med Rehabil. 2003;84(Suppl 2):S43–S51
  9. Velozo CA, Kielhofner G, Lai J-S. The use of Rasch analysis to produce scale-free measurement of functional activity. Am J Occup Ther. 1999;53:83–90
  10. In:  Sands WA,  Waters BK,  McBride JR editor. Computerized adaptive testing: from inquiry to operation. Washington, DC: American Psychological Association; 1997;
  11. Dodd BG, De Ayala RJ, Koch WR. Computerized adaptive testing with polytomous items. Appl Psychol Meas. 1995;19:5–22
  12. Cook KF, Roddey TS, Gartsman GM, Olson SL. Development and psychometric evaluation of the Flexilevel Scale of Shoulder Function. Med Care. 2003;41:823–835
  13. Lord FM. The self-scoring flexilevel test. J Educ Meas. 1971;8:147–151
  14. Lord FM. Applications of item response theory to practical testing problems. Hillsdale, NJ: Lawrence Erlbaum; 1980;
  15. Hart DL, Wright BD. Development of an index of physical functional health status in rehabilitation. Arch Phys Med Rehabil. 2002;83:655–665
  16. World Health Organization . International classification of functioning, disability and health. Geneva: World Health Organization; 2001;
  17. Hays RD, Morales LS, Reise SP. Item response theory and health outcomes measurement in the 21st century. Med Care. 2000;38(9 Suppl):II-28–42
  18. Holland PW. When are item response models consistent with observed data?. Psychometrika. 1981;46:79–92
  19. Holland PW, Rosenbaum PR. Conditional association and unidimensionality in monotone latent variable models. Ann Stat. 1986;14:1523–1543
  20. Rosenbaum PR. Testing the conditional independence and monotonicity assumptions of item response theory. Psychometrika. 1984;49:425–435
  21. Lazarsfeld PF, Henry NW. Latent structure analysis. Boston, MA: Houghton Mifflin; 1968;
  22. Wainer H, Mislevy RJ. Item response theory, item calibration, and proficiency estimation. In:  Wainer H, et al. editor. Computerized adaptive testing: a primer. 2nd ed. Mahway, NJ: Lawrence Erlbaum Associates; 2000;p. 61–100
  23. Hambleton RK, Swaminathan H, Rogers HJ. Fundamentals of item response theory. Newbury Park, CA: Sage Publications; 1991;
  24. Stout WF. A new item response theory modeling approach with applications to unidimensionality assessment and ability estimation. Psychometrika. 1990;55:293–325
  25. Muthén LK, Muthén BO. Mplus: Statistical analysis with latent trait variables. v 2.14. [computer software] Los Angeles, CA: Muthén & Muthén; 2001;
  26. Bjorner JB, Kosinski M, Ware JE. The feasibility of applying item response theory to measures of migraine impact: a re-analysis of three clinical studies. Qual Life Res. 2003;12:887–902
  27. McDonald RP. Test theory: a unified treatment. Mahway, NJ: Lawrence Erlbaum; 1999;
  28. Kline RB. Principles and practice of structural equation modeling. New York: Guilford Press; 1998;
  29. Bentler P. Comparative fit indices in structural models. Psychol Bull. 1990;107:238–246
  30. West SG, Finch JF, Curran PJ. SEM with nonnormal variables. In:  Hoyle RH editors. Structural equation modeling: concepts, issues and applications. Thousand Oaks, CA: Sage Publications; 1995;p. 56–75
  31. Hu LT, Bentler P. Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Struct Equation Model. 1999;6:1–55
  32. Browne MW, Cudeck R. Alternative ways of assessing model fit. In:  Bollen KA,  Long JS editor. Testing structural equation models. Newbury Park, CA: Sage Publications; 1993;
  33. March HW, Balla JR, Hau K. An evaluation of increment fit indices: a clarification of mathematical and empirical properties. In:  Marcoulides GA,  Schumacker RE editor. Advanced structural equation modeling: issues and techniques. Mahwah, NJ: Lawrence Erlbaum; 1996;p. 315–353
  34. Andrich D. A rating formulation for ordered response categories. Psychometrika. 1978;43:561–573
  35. Rasch G. Probabilistic models for some intelligence and attainment tests. Chicago, IL: MESA Press; 1980;
  36. McHorney CA, Monahan PO. Postscript: applications of Rasch analysis in health care. Med Care. 2004;42(1 Suppl):I-73–8
  37. Wright BD, Masters GN. Rating scale analysis. Chicago, IL: MESA Press; 1982;
  38. Masters GN. A Rasch model for partial credit scoring. Psychometrika. 1982;47:149–174
  39. Dodd BG. The effect of item selection procedure and stepsize on computerized adaptive attitude measurement using the rating scale model. Appl Psychol Meas. 1990;14:355–366
  40. Andersen EB. The rating scale model. In:  van der Linden WJ,  Hambleton RK editor. Handbook of modern item response theory. New York: Springer-Verlag; 1997;p. 67–84
  41. Haley SM, McHorney CA, Ware JE. Evaluation of the MOS SF-36 physical functioning scale (PF-10): I. Unidimensionality and reproducibility of the Rasch item scale. J Clin Epidemiol. 1994;47:671–684
  42. Linacre JM. A user's guide to WINSTEPS. v 3.55. Chicago, IL: MESA Press; 2005;
  43. Bond TG, Fox CM. Applying the Rasch model: fundamental measurement in the human sciences. Mahwah, NJ: Lawrence Erlbaum Associates; 2001;
  44. Thissen D, Mislevy RJ. Testing algorithms. In:  Wainer H editors. Computerized adaptive testing: a primer. 2nd ed. Mahway, NJ: Lawrence Erlbaum Associates; 2000;p. 101–134
  45. Hart DL, Mioduski JE. CAT development and testing software user's guide. Knoxville, TN: FOTO; 2004;
  46. Linacre JM. Estimating measures with known polytomous item difficulties. Rasch Meas Trans. 1998;12:638
  47. McHorney CA, Ware JE, Rogers W, Raczek AE, Lu JF. The validity and relative precision of MOS short and long form health status scales and Dartmouth COOP charts: results from the Medical Outcomes Study. Med Care. 1992;30:MS253–MS265
  48. Werneke M, Hart DL. Discriminant validity and relative precision for classifying patients with nonspecific neck and back pain by anatomic pain patterns. Spine. 2003;28:161–166
  49. Norquist JM, Fitzpatrick R, Dawson J, Jenkinson C. Comparing alternative Rasch-based methods vs raw scores in measuring change in health. Med Care. 2004;42(1 Suppl):I-25–36
  50. Fitzpatrick R, Norquist JM, Dawson J, Jenkinson C. Rasch scoring of outcomes of total hip replacement. J Clin Epidemiol. 2003;56:68–74
  51. McHorney CA, Haley SM, Ware JE. Evaluation of the MOS SF-36 physical functioning scale (PF-10): II. Comparison of relative precision using Likert and Rasch scoring methods. J Clin Epidemiol. 1997;50:451–461
  52. Mallinson T, Stelmack J, Velozo C. A comparison of the separation ratio and coefficient α in the creation of minimum item sets. Med Care. 2004;42(1 Suppl):I-17–24
  53. Chang HH, Ying Z. α-Stratified multistage computerized adaptive testing. Appl Psychol Meas. 1999;23:211–222
  54. Crane PK, Hart DL, Gibbons LE, Cook KF. A 37-item shoulder functional status item pool had negligible differential item functioning. J Clin Epidemiol, in press, 2005.
  55. McHorney CA, Cohen AS. Equating health status measures with item response theory: illustrations with functional status items. Med Care. 2000;38(9 Suppl):II-43–59
  56. McHorney CA. Use of item response theory to link 3 modules of functional status from the Asset and Health Dynamics Among the Oldest Old Study. Arch Phys Med Rehabil. 2002;83:383–394
  57. Dodd BG, Koch WR, DeAyala RJ. Operational characteristics of adaptive testing procedures using the graded response model. Appl Psychol Meas. 1989;13:129–143

PII: S0895-4356(05)00298-2

doi: 10.1016/j.jclinepi.2005.08.006

Journal of Clinical Epidemiology
Volume 59, Issue 3 , Pages 290-298 , March 2006