Quality of Life Research

, Volume 17, Issue 1, pp 75–85 | Cite as

Comparison of anchor-based and distributional approaches in estimating important difference in common cold

Article

Abstract

Context

Evaluative health-related quality-of-life instruments used in clinical trials should be able to detect small but important changes in health status. Several approaches to minimal important difference (MID) and responsiveness have been developed.

Objectives

To compare anchor-based and distributional approaches to important difference and responsiveness for the Wisconsin Upper Respiratory Symptom Survey (WURSS), an illness-specific quality of life outcomes instrument.

Design

Participants with community-acquired colds self-reported daily using the WURSS-44. Distribution-based methods calculated standardized effect size (ES) and standard error of measurement (SEM). Anchor-based methods compared daily interval changes to global ratings of change, using: (1) standard MID methods based on correspondence to ratings of “a little better” or “somewhat better,” and (2) two-level multivariate regression models.

Participants

About 150 adults were monitored throughout their colds (1,681 sick days.): 88% were white, 69% were women, and 50% had completed college. The mean age was 35.5 years (SD = 14.7).

Results

WURSS scores increased 2.2 points from the first to second day, and then dropped by an average of 8.2 points per day from days 2 to 7. The SEM averaged 9.1 during these 7 days. Standard methods yielded a between day MID of 22 points. Regression models of MID projected 11.3-point daily changes. Dividing these estimates of small-but-important-difference by pooled SDs yielded coefficients of .425 for standard MID, .218 for regression model, .177 for SEM, and .157 for ES. These imply per-group sample sizes of 870 using ES, 616 for SEM, 302 for regression model, and 89 for standard MID, assuming α = .05, β = .20 (80% power), and two-tailed testing.

Conclusions

Distribution and anchor-based approaches provide somewhat different estimates of small but important difference, which in turn can have substantial impact on trial design.

Keywords

Clinical significance Common cold Evidence-based medicine Health status Minimal important difference Psychometrics Quality of life Questionnaires Respiratory tract infections Severity of illness index Symptom measurement Treatment outcome Upper respiratory infection 

References

  1. 1.
    McDowell, I., & Newell, C. (1996). Measuring health: A guide to rating scales and questionnaires. Oxford & New York: Oxford University Press.Google Scholar
  2. 2.
    Kirshner, B., & Guyatt, G. H. (1985). A methodological framework for assessing health indices. Journal of Chronic Diseases, 38, 27–36.PubMedCrossRefGoogle Scholar
  3. 3.
    Jaeschke, R., Singer, J., & Guyatt, G. H. (1989). Measurement of health status: Ascertaining the minimal clinically important difference. Controlled Clinical Trials, 10, 407–415.PubMedCrossRefGoogle Scholar
  4. 4.
    Powell, C. V., & Kelly, A.-M. (2001). Determining the minimum clinically significant difference in visual analog pain score for children. Annals of Emergency Medicine, 37, 28–31.PubMedCrossRefGoogle Scholar
  5. 5.
    Redelmeier, D. A., Guyatt, G. H., & Goldstein, R. S. (1996). Assessing the minimal important difference in symptoms: A comparison of two techniques. Journal of Clinical Epidemiology, 49, 1215–1219.PubMedCrossRefGoogle Scholar
  6. 6.
    Santanello, N. C., Zhang, J., Seidenberg, B., Reiss, T. F., & Barber, B. L. (1999). What are minimal important changes for asthma measures in a clinical trial? European Respiratory Journal, 14, 23–27.PubMedCrossRefGoogle Scholar
  7. 7.
    Schunemann, H. J., Griffith, L., Jaeschke, R., Goldstein, R., Stubbing, D., & Guyatt, G. H. (2003). Evaluation of the minimal important difference for the feeling thermometer and the St. George’s Respiratory Questionnaire in patients with chronic airflow obstruction. Journal of Clinical Epidemiology, 56, 1170–1176.PubMedCrossRefGoogle Scholar
  8. 8.
    van Stel, H. F., Maille, A. R., Colland, V. T., & Everaerd, W. (2003). Interpretation of change and longitudinal validity of the quality of life for respiratory illness questionnaire (QoLRIQ) in inpatient pulmonary rehabilitation. Quality of Life Research, 12, 133–145.PubMedCrossRefGoogle Scholar
  9. 9.
    van Walraven, C., Mahon, J. L., Moher, D., Bohm, C., & Laupacis, A. (1999). Surveying physicians to determine the minimal important difference: Implications for sample-size calculation. Journal of Clinical Epidemiology, 52, 717–723.PubMedCrossRefGoogle Scholar
  10. 10.
    Beaton, D. E., Bombardier, C., Katz, J. N., & Wright, J. G. (2001). A taxonomy for responsiveness. Journal of Clinical Epidemiology, 54, 1204–1207.PubMedCrossRefGoogle Scholar
  11. 11.
    Brant, R., Sutherland, L., & Hilsden, R. (1999). Examining the minimum important difference. Statistics in Medicine, 18, 2593–2603.PubMedCrossRefGoogle Scholar
  12. 12.
    Deyo, R. A., & Centor, R. M. (1986). Assessing the responsiveness of functional scales to clinical change: An analogy to diagnostic test performance. Journal of Chronic Diseases, 39, 897–906.PubMedCrossRefGoogle Scholar
  13. 13.
    Frost, M. H., Bonomi, A. E., Ferrans, C. E., Wong, G. Y., & Hays, R. D. (2002). Clinical Significance Consensus Meeting Group. Patient, clinician, and population perspectives on determining the clinical significance of quality-of-life scores. Mayo Clinic Proceedings, 77, 488–494.PubMedCrossRefGoogle Scholar
  14. 14.
    Guyatt, G. H., Osoba, D., Wu, A. W., Wyrwich, K. W., & Norman, G. R. (2002). Clinical Significance Consensus Meeting Group. Methods to explain the clinical significance of health status measures. Mayo Clinic Proceedings, 77, 371–383.PubMedGoogle Scholar
  15. 15.
    Norman, G. R., Stratford, P., & Regehr, G. (1997). Methodological problems in the retrospective computation of responsiveness to change: The lesson of Cronbach. Journal of Clinical Epidemiology, 50, 869–879.PubMedCrossRefGoogle Scholar
  16. 16.
    Norman, G. R., Sridhar, F. G., Guyatt, G. H., & Walter, S. D. (2001). Relation of distribution- and anchor-based approaches in interpretation of changes in health-related quality of life. Medical Care, 39, 1039–1047.PubMedCrossRefGoogle Scholar
  17. 17.
    Samsa, G. (2001). How should the minimum important difference for a health-related quality-of-life instrument be estimated? Medical Care, 39, 1037–1038.PubMedCrossRefGoogle Scholar
  18. 18.
    Husted, J. A., Gladman, D. D., Cook, R. J., & Farewell, V. T. (1998). Responsiveness of health status instruments to changes in articular status and perceived health in patients with psoriatic arthritis. Journal of Rheumatology, 25, 2146–2155.PubMedGoogle Scholar
  19. 19.
    Guyatt, G. H., Walter, S., & Norman, G. (1987). Measuring change over time: Assessing the usefulness of evaluative instruments. Journal of Chronic Diseases, 40, 171–178.PubMedCrossRefGoogle Scholar
  20. 20.
    Juniper, E. F., & .Guyatt, G. H. (1991). Development and testing of a new measure of health status for clinical trials in rhinoconjunctivitis. Clinical & Experimental Allergy, 21, 77–83.CrossRefGoogle Scholar
  21. 21.
    Juniper, E. F., Guyatt, G. H., Willan, A., & Griffith, L. E. (1994). Determining a minimal important change in a disease-specific Quality of Life Questionnaire. Journal of Clinical Epidemiology, 47, 81–87.PubMedCrossRefGoogle Scholar
  22. 22.
    Wells, G. A., Tugwell, P., Kraag, G. R., Baker, P. R., Groh, J., & Redelmeier, D. A. (1993). Minimum important difference between patients with rheumatoid arthritis: the patient’s perspective. Journal of Rheumatology, 20, 557–560.PubMedGoogle Scholar
  23. 23.
    Todd, K. H., & Funk, J. P. (1996). The minimum clinically important difference in physician-assigned visual analog pain scores. Academic Emergency Medicine, 3, 142–146.PubMedGoogle Scholar
  24. 24.
    Bruynesteyn, K., van der Heijde, H. D., Boers, M., Lassere, M., Boonen, A., Edmonds, J, et al. (2001). Minimal clinically important difference in radiological progression of joint damage over 1 year in rheumatoid arthritis: Preliminary results of a validation study with clinical experts. Journal of Rheumatology, 28, 904–910.PubMedGoogle Scholar
  25. 25.
    Bombardier, C., Hayden, J., & Beaton, D. E. (2001). Minimal clinically important difference, low back pain: Outcomes measures. Journal of Rheumatology, 28, 431–438.PubMedGoogle Scholar
  26. 26.
    Farrar, J. T., Portenoy, R. K., Berlin, J. A., Kinman, J. L., & Strom, B. L. (2000). Defining the clinically important difference in pain outcome measures. Pain, 88, 287–294.PubMedCrossRefGoogle Scholar
  27. 27.
    Kazis, L. E., Anderson, J. L., Meenan, R. F. (1989). Effect sizes for interpreting changes in health status. Medical Care, 27(Suppl), S178–S189.PubMedCrossRefGoogle Scholar
  28. 28.
    Ward, M. M., Marx, A. S., & Barry, N. N. (2000). Identification of clinically important changes in health status using receiver operating characteristic curves. Journal of Clinical Epidemiology, 53, 279–284.PubMedCrossRefGoogle Scholar
  29. 29.
    Wyrwich, K. W., Tierney, W. M., & Wolinsky, F. D. (2002). Using the standard error of measurement to identify important changes on the Asthma Quality of Life Questionnaire. Quality of Life Research, 11, 1–7.PubMedCrossRefGoogle Scholar
  30. 30.
    Cohen, J. (1988) Statistical power analysis for the behavioral sciences. Hillsdale, N.J.: Lawrence Erlbaum Associates.Google Scholar
  31. 31.
    Norman, G. R., Wyrwich, K. W., & Patrick, D. L. (2007). The mathematical relationship among different forms of responsiveness coefficients. Quality of Life Research, 16(5), 815–822.PubMedCrossRefGoogle Scholar
  32. 32.
    Wyrwich, K. W., Nienaber, N. A., Tierney, W. M., & Wolinsky, F. D. (1999). Linking clinical relevance and statistical significance in evaluating intra-individual changes in health-related quality of life. Medical Care, 37, 469–478.PubMedCrossRefGoogle Scholar
  33. 33.
    Wyrwich, K. W. (2004). Minimal important difference thresholds and the standard error of measurement: Is there a connection? Journal of Biopharmaceutical Statistics, 14, 97–110.PubMedCrossRefGoogle Scholar
  34. 34.
    Barrett, B., Locken, K., Maberry, R., Schwamman, J., Bobula, J., Brown, R., et al. (2002). The Wisconsin Upper Respiratory Symptom Survey: Development of an instrument to measure the common cold. Journal of Family Practice, 51, 265–273.PubMedGoogle Scholar
  35. 35.
    Barrett, B. P., Brown, R. L., Locken, K., Maberry, R., Bobula, J. A., & D’Alessio, D. (2002). Treatment of the common cold with unrefined echinacea: A randomized, double-blind, placebo-controlled trial. Annals of Internal Medicine, 137, 939–946.PubMedGoogle Scholar
  36. 36.
    Barrett, B., Brown, R., Mundt, M., Safdar, N., Dye, L., Maberry, R., et al. (2005). The Wisconsin Upper Respiratory Symptom Survey is responsive, reliable, and valid. Journal of Clinical Epidemiology, 58, 609–617.PubMedCrossRefGoogle Scholar
  37. 37.
    Jackson, G. G., Dowling, H. F., & Muldoon, R. L. (1962). Present concepts of the common cold. American Journal of Public Health, 52, 940–945.PubMedGoogle Scholar
  38. 38.
    McHorney, C. A., Ware, J. E., & Raczek, A. E. (1998). The MOS 36-item short-form health survey (SF-36): II. Psychometric and clinical tests of validity in measuring physical and mental health constructs. Medical Care, 31, 247–263.CrossRefGoogle Scholar
  39. 39.
    Ware, J. E, Kosinski, M., Dewey, J. E., & Gandek, B. (2001) How to score and interpret single-item health status measures: A manual for users of the SF-8 health survey. Lincoln RI: Quality Metric.Google Scholar
  40. 40.
    Barrett, B., Brown, R., Voland, R., Maberry, R., & Turner, R. (2006). Relations among questionnaire and laboratory measures of rhinovirus infection. European Respiratory Journal, 28, 358–363.PubMedCrossRefGoogle Scholar
  41. 41.
    Yang, M., & Goldstein, H. (1996). Multilebel models for longitudinal data. In U. Engel & J. Tanner (Eds.), Analysis of change: Advanced techniques in panel data analysis (pp. 191–220). Berlin: Walter de Gruyter.Google Scholar
  42. 42.
    Jaeschke, R., Singer, J., & Guyatt, G. H. (1990). A comparison of seven-point and visual analogue scales. Data from a randomized trial. Controlled Clinical Trials, 11, 43–51.PubMedCrossRefGoogle Scholar
  43. 43.
    Miller, G. A. (1956). The magical number seven plus or minus two: some limits on our capacity for processing information. Psychological Review, 63, 81–97.PubMedCrossRefGoogle Scholar
  44. 44.
    Froberg, D. G., & Kane, R. L. (1989). Methodology for measuring health-state preferences-II: Scaling methods. Journal of Clinical Epidemiology, 42, 459–471.PubMedCrossRefGoogle Scholar
  45. 45.
    Cohen, J. (1969). Statistical power analysis for the behavioural sciences. London: Academic Press.Google Scholar
  46. 46.
    Gwaltney, J. M Jr., Hendley, J. O., & Patrie, J. T. (2003). Symptom severity patterns in experimental common colds and their usefulness in timing onset of illness in natural colds. Clinical Infectious Diseases, 36, 714–723.PubMedCrossRefGoogle Scholar
  47. 47.
    Cronbach, L. J. (1957). The two disciplines of scientific psychology. American Psychologist, 12, 671–684.CrossRefGoogle Scholar
  48. 48.
    Gleser, G. C., Cronbach, L. J., & Rajaratnam, N. (1965). Generalizability of scores influenced by multiple sources of variance. Psychometrika, 30, 395–418.PubMedCrossRefGoogle Scholar
  49. 49.
    Cronbach, L. J., & Furby, L. (1970). How should we measure “change” – Or should we? Psychological Bulletin, 74, 68–80.CrossRefGoogle Scholar
  50. 50.
    Deyo, R. A., & Inui, T. S. (1984). Toward clinical applications of health status measures: sensitivity of scales to clinically important changes. Health Services Research, 19, 277–289.Google Scholar
  51. 51.
    Ross, M. (1989). Relation of implicit theories to the construction of personal histories. Psychological Review, 96, 341–347.CrossRefGoogle Scholar
  52. 52.
    Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211, 453–458.PubMedCrossRefGoogle Scholar
  53. 53.
    Norman, G. R., Sloan, J. A., & Wyrwich, K. W. (2003). Interpretation of changes in health-related quality of life: The remarkable universality of half a standard deviation. Medical Care, 41, 582–592.PubMedCrossRefGoogle Scholar
  54. 54.
    Norman, G. R. (2005). The relation between the minimally important difference and patient benefit. COPD, 2, 69–73.PubMedCrossRefGoogle Scholar
  55. 55.
    Llewellyn-Thomas, H. A., Williams, J. I., Levy, L., & Naylor, C. D. (1996). Using a trade-off technique to assess patients’ treatment preferences for benign prostatic hyperplasia. Medical Decision Making, 16, 262–282.PubMedCrossRefGoogle Scholar
  56. 56.
    Naylor, C. D., & Llewellyn-Thomas, H. A. (1994). Can there be a more patient-centred approach to determining clinically important effect sizes for randomized treatment trials? Journal of Clinical Epidemiology, 47, 787–795.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2007

Authors and Affiliations

  1. 1.Department of Family MedicineUniversity of Wisconsin Medical SchoolMadisonUSA

Personalised recommendations