Quality of Life Research

, Volume 13, Issue 1, pp 35–44

Content-based interpretation aids for health-related quality of life measures in clinical practice. An example for the visual function index (VF-14)

  • J.M. Valderas
  • J. Alonso
  • L. Prieto
  • M. Espallargues
  • X. Castells
Article

Abstract

Background: In spite of a well-established development of instruments, difficulty in interpreting health related quality of life scores may limit its use in clinical practice. Objective: To develop generalizable interpretation aids for a measure of perceived functional visual status, the VF-14 index. Design: Item Response Theory (Rasch analysis) was used to analyze the performance of VF-14 items. The ‘ruler’ aid was derived from the most difficult activity (item) a patient is able to do without difficulty; the ‘clinical scenarios’ aid, first identified all significantly different clusters of items within the index and then estimated the mean expected difficulty (responses) to perform a benchmark item in each cluster. Setting: The study was conducted in four hospitals and six ambulatory cataract surgery centers in Barcelona, Spain. Patients: One hundred and ninety-eight patients scheduled for first eye cataracts surgery. Measurements: The self-reported VF-14 index and clinical measures were used. Results: All VF-14 items were found unidimensional with three items showing only partial misfit. For a patient with a VF-14 Rasch score of 71, the ‘ruler’ aid indicated that ‘doing fine handwork’ would be the most requiring activity he/she would perform without difficulty. The ‘clinical scenarios’ aid estimated that such a patient would be unable to ‘drive at night’, would have some difficulty ‘reading small print’ and no difficulty ‘doing fine handwork’, ‘watching TV’ or ‘recognizing people’. Concordance between modeled and observed responses was fair to substantial. Conclusions: Simple content-based interpretation aids for the VF-14 scores were developed that should facilitate its use in clinical practice. These aids should be easily generalizable to other quality of life instruments.

Clinical interpretability Functional status Health-related quality of life Item response theory Psychometric methods Questionnaires Validity 

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References

  1. 1.
    Fletcher A, Gore S, Jones D, Fitzpatrick R, Spiegelhalter D, Cox D. Quality of life measures in health care. II: Design, analysis, and interpretation. Br Med J 1992; 305: 1145-1148.Google Scholar
  2. 2.
    Clancy CM, Eisenberg JM. Outcomes research: Measuring the end results of health care. Science 1998; 282(5387): 245-246.Google Scholar
  3. 3.
    Espallargues M, Valderas JM, Alonso J. Provision of feedback on perceived health status to health care professionals. A systematic review of its impact. Med Care 2000; 38(2): 175-186.Google Scholar
  4. 4.
    Osoba D, Till JE, Pater JL, Young JR. Health-related quality of life: Measurement and clinical application. A workshop report. Can J Oncol 1995; 5(1): 338-343.Google Scholar
  5. 5.
    Lydick E. Approaches to the interpretation of quality-of-life scales. Med Care 2000; 38(Suppl 9): II180-II183.Google Scholar
  6. 6.
    Ware JE, Keller SD. Interpreting General Health Measures. In: Spilker B (ed), Quality of Life and Pharmaeconomics in Clinical Trials, Philadelphia: Lippincott-Raven Publishers, 1996; 445-460.Google Scholar
  7. 7.
    Ferrer M, Alonso J, Morera J, et al. Chronic obstructive pulmonary disease stage and health-related quality of life. Ann Intern Med 1997; 127(12): 1072-1079.Google Scholar
  8. 8.
    Nunnally JC, Bernstein IH. Psychometric Theory. 3rd ed. New York: McGraw-Hill, 1994.Google Scholar
  9. 9.
    Ware JE, Snow KK, Kosinski M, Gandek B. SF-36 Health Survey Manual and Interpretation Guide. Boston, MA: The Health Institute, 1993.Google Scholar
  10. 10.
    Crum RM, Anthony JC, Bassett SS, Folstein MF. Population-based norms for the Mini-Mental State Examination by age and educational level. JAMA 1993; 269(18): 2386-2391.Google Scholar
  11. 11.
    Klee M, Groenvold M, Machin D. Quality of life of Danish women: Population-based norms of the EORTC QLQ-C30. Qual Life Res 1997; 6(1): 27-34.Google Scholar
  12. 12.
    Stewart AL, Greenfield S, Hays RD, et al. Functional Status and Well-being of Patients with Chronic Conditions. Results from the Medical Outcomes Study. JAMA 1989; 262(7): 907-913.Google Scholar
  13. 13.
    Kazis LE, Anderson JJ, Meenan RF. Effect sizes for interpreting changes in health status. Med Care 1989; 27(Suppl 3): S178-S189.Google Scholar
  14. 14.
    Juniper EF, Gordon GH, Willan A, Griffith LE. Determining a mininal important change in a disease-specific quality of life questionnaire. J Clin Epidemiol 1994; 47(1): 81-87.Google Scholar
  15. 15.
    Wyrwich KW, Tierney WM, Wolinsky FD. Further evidence supporting an SEM-based criterion for identifying meaningful intra-individual changes in health-related quality of life. J Clin Epidemiol 1999; 52(9): 861-873.Google Scholar
  16. 16.
    Guyatt GH, Juniper EF, Walter SD, Griffith LE, Goldstein RS. Interpreting treatment effects in randomised trials. Br Med J 1998; 316(7132): 690-693.Google Scholar
  17. 17.
    Wyrwich KW, Wolinsky FD. Identifying meaningful intra-individual change standards for health-related quality of life measures. J Eval Clin Pract 2000; 6(1): 39-49.Google Scholar
  18. 18.
    Deyo RA, Patrick DL. The significance of treatment effects: The clinical perspective. Med Care 1995; 33(Suppl 4): AS286-AS291.Google Scholar
  19. 19.
    Wright BD, Stone MH. Best Test Design. Chicago: MESA Press, 1979.Google Scholar
  20. 20.
    Wright BD, Masters GN. Rating Scale Analysis. Chicago: Mesa Press, 1982.Google Scholar
  21. 21.
    Hays RD, Morales LS, Reise SP. Item response theory and health outcomes measurement in the 21st century. Med Care 2000; 38(Suppl 9): II28-II42.Google Scholar
  22. 22.
    Coster W, Ludlow L, Mancini M. Using IRT variable maps to enrich understanding of rehabilitation data. J Outcome Meas 1999; 3(2): 123-133.Google Scholar
  23. 23.
    Haley SM, McHorney CA, Ware JE Jr. 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(6): 671-684.Google Scholar
  24. 24.
    Fisher WP Jr. A research program for accountable and patient-centered health outcome measures. J Outcome Meas 1998; 2(3): 222-239.Google Scholar
  25. 25.
    Steinberg EP, Tielsch JM, Schein OD, et al. The VF-14: An index of functional impairment in patients with cataract. Arch Ophthalmol 1994; 112: 630-638.Google Scholar
  26. 26.
    Cassard SD, Patrick DL, Damiano AM, et al. Reproducibility and responsiveness of the VF-14. An index of functional impairment in patients with cataracts. Arch Ophthalmol 1995; 113: 1508-1513.Google Scholar
  27. 27.
    Alonso J, Espallargues M, Folmer-Andersen T, et al. International applicability of the VF-14. An Index of Visual Function in Patients with Cataracts. Ophthalmology 1997; 104(5): 799-807.Google Scholar
  28. 28.
    Espallargues M, Alonso J. Effectiveness of cataract surgery in Barcelona, Spain site results of an international study. Barcelona I-PORT investigators. International Patient Outcomes Research Team. J Clin Epidemiol 1998; 51(10): 843-852.Google Scholar
  29. 29.
    Andrich D, de Jong JHAL, Sheridan BE. Diagnostic opportunities with the Rasch model for ordered response categories. In: Rost J, Kangeheine R (eds), Applications of Latent Trait and Latent Class Models in the Social Sciences 1997; 59-70.Google Scholar
  30. 30.
    Wright BD, Linacre JM. Bigsteps: Rasch Analysis for all two-facet models (2.73). 1997.Google Scholar
  31. 31.
    Dawson TL. Moral and evaluative reasoning across the Life span. J Appl Meas 2000; 1(4): 346-371.Google Scholar
  32. 32.
    Wright BD, Linacre JM. A user's guide to BIGSTEPS: Rasch-Model Computer Program, version 2.7. Chicago: MESA Press, 1997.Google Scholar
  33. 33.
    Deyo RA, Diehr PD, Patrick DL. Reproducibility and responsiveness of health status measures. Statistics and strategies for evaluation. Controll Clin Trials 1991; 12(Suppl 4): 142-158.Google Scholar
  34. 34.
    Linacre JM. Prioritizing misfit indicators. Rasch Measurement Transactions 1995; 9(2): 1992; 422-423.Google Scholar
  35. 35.
    Apostol TM. Calculus. 2nd ed. Barcelona: Ed. Reverté, 1973.Google Scholar
  36. 36.
    Landis JR, Koch GG. The measurement of observer agreement for categorical data. Biometrics 1977; 33: 159-174.Google Scholar
  37. 37.
    Fisher WP Jr. Foundations for health status metrology: The stability of MOS SF-36 PF-10 calibrations across samples. J LA State Med Soc 1999; 151(11): 566-578.Google Scholar
  38. 38.
    Prieto L, Alonso J, Lamarca R, Wright BD. Rasch measurement for reducing the items of the Nottingham Health Profile. J Outcome Meas 1998; 2(4): 285-301.Google Scholar
  39. 39.
    Morales LS, Reise SP, Hays RD. Evaluating the equivalence of health care ratings by whites and Hispanics. Med Care 2000; 38(5): 517-527.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • J.M. Valderas
    • 1
  • J. Alonso
    • 1
    • 2
  • L. Prieto
    • 1
  • M. Espallargues
    • 3
  • X. Castells
    • 1
    • 2
  1. 1.Health Services Research UnitInstitut Municipal d'Investigació Mèdica (IMIM-IMAS)BarcelonaSpain
  2. 2.Universitat Autònoma de BarcelonaSpain
  3. 3.Catalan Agency for Health Technology Assessment and ResearchBarcelonaSpain

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