Skip to main content
SpringerLink
Log in

Interpretability, validity, and the minimum important difference

  • Published:
Theoretical Medicine and Bioethics Aims and scope Submit manuscript

Abstract

Patient-reported outcomes are increasingly used as dependent variables in studies regarding the effectiveness of clinical interventions. But patient-reported outcome measures (PROMs) do not provide intuitively meaningful data. For instance, it is not clear what a five point increase or decrease on a particular scale signifies. Establishing ‘interpretability’ involves making changes in outcomes meaningful. Attempts to interpret PROMs have led to the development of methods for identifying a minimum important difference (MID). In this paper, however, I draw on Charles Taylor’s distinction between weak and strong evaluations to suggest that identifying a MID, specifically, a MID that uses a patient-reported reference group, may not provide an adequate interpretation of these measures. Moreover, I argue that the difficulty with interpreting these measures is tied to a larger problem concerning their validity. If researchers wish to interpret PROMs, they may first need to know more about the constructs they attempt to measure, namely, quality of life.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. To be sure, one might understand this respondent’s answer differently. For instance, as Weinfurt et al. [16] have argued, in the context of informing patients about the risks of Phase I Clinical Trials, there is the possibility of multiple speech acts in any speech episode. The respondent above may intend to convey something different than her words seem to suggest. For instance, she may not aim to provide an evaluation of the place that worry has in a good life, but may instead aim to exude confidence in her radiotherapy or reinforce a positive outlook on her treatment.

  2. In the glossary of the Food and Drug Administration’s 2009 Guidance for Industry Patient-Reported Outcome Measures: Use in Medical Product Development to Support Labeling Claims [21], the FDA defines quality of life as a “general concept that implies an evaluation on the effect of all aspects of life on general well-being.” It goes on to state that because this term involves the evaluation of non-health related aspects of life, and because the term is generally taken to mean what the patient thinks it means, quality of life is too general and undefined to be used for a medical product claim. Part of my argument in this paper, however, is that non-health related aspects of life do impinge on subjective evaluations of health and that these evaluations involve assessments about quality. Moreover, many PROMs do aim to assess quality of life, e.g., European Organization for Research and Treatment of Cancer, Quality of Life Questionnaire, EuroQol, Asthma Quality of Life Questionnaire. Furthermore, many other PROMs include constructs that are associated with quality of life, e.g., social and emotional functioning. These instruments are commonly referred to as ‘Quality of Life (QoL) instruments’; see, e.g., [6].

  3. The phenomenon of response shift, which I discussed earlier, is sometimes taken to contaminate respondent answers. See, e.g., [24].

  4. Many PROMs aim to measure quality of life, but because quality of life is generally taken to be a multidimensional construct, these measures often have multiple scales. Each scale is in turn associated with its own construct, e.g., emotional functioning, mobility, fatigue. The constructs from each scale together provide an assessment of the larger construct, i.e., quality of life.

References

  1. Hobart, J.C., S.J. Cano, J.P. Zajicek, and A.J. Thompson. 2007. Rating scales as outcome measures in clinical trials in neurology: Problems, solutions and recommendations. Lancet Neurology 6: 1094–1105.

    Article  Google Scholar 

  2. Sechrest, L., P. McKnight, and K. McKnight. 1996. Calibration of measures for psychotherapy outcomes studies. American Psychologist 51: 1065–1071.

    Article  Google Scholar 

  3. Guyatt, G.H. 2000. Making sense of quality-of-life data. Medical Care 38(9 Suppl): II-175–II-179.

    Google Scholar 

  4. Norman, G.R., J.A. Sloan, and K.W. Wyrwich. 2003. Interpretation of changes in health-related quality of life: The remarkable universality of half a standard deviation. Medical Care 41: 582–592.

    Google Scholar 

  5. Jaeschke, R., J. Singer, and G.H. Guyatt. 1989. Measurement of health status: Ascertaining the minimal clinically important difference. Control Clinical Trials 10: 407–415.

    Article  Google Scholar 

  6. Fayers, P.M., and D. Machin. 2007. Quality of life: The assessment, analysis and interpretation of patient-reported outcomes, 2nd ed. Chichester: Wiley.

    Google Scholar 

  7. Testa, M.A. 2000. Interpretation of quality of life outcomes, issues that affect magnitude and meaning. Medical Care 38(9 Suppl): II-166–II-174.

    Google Scholar 

  8. Brozek, J.L., G.H. Guyatt, and H.J. Schunemann. 2006. How a well-grounded minimal important difference can enhance transparency of labeling claims and improve interpretation of a patient reported outcome measure. Health and Quality of Life Outcomes 4: 69.

    Article  Google Scholar 

  9. Ross, M. 1989. Relation of implicit theories to the construction of personal histories. Psychological Review 96: 341–357.

    Article  Google Scholar 

  10. Middel, B., H. Goudriaan, M. de Greef, R. Stewart, E. van Sonderen, J. Bouma, and M. de Jongste. 2006. Recall bias did not affect perceived magnitude of change in health-related functional status. Jounal of Clinical Epidemiology 59: 503–511.

    Article  Google Scholar 

  11. Schwartz, C.E., and M.A.G. Sprangers. 1999. Methodological approaches for assessing response shift in longitudinal health-related quality of life research. Science and Social Medicine 48: 1531–1548.

    Article  Google Scholar 

  12. Taminiau-Bloem, E.F., F.J. van Zuuren, M.R.M. Visser, et al. Forthcoming. Opening the black box of cancer patients’ quality-of-life change assessments: A qualitative study examining the cognitive processes underlying responses to transition items. Psychology and Health.

  13. Krabbe, P.F.M. 2008. Thurstone scaling as a measurement method to quantify subjective health outcomes. Medical Care 46: 357–365.

    Article  Google Scholar 

  14. Clark, H.H., and M.F. Schober. 1992. Asking questions and influencing answers. In Questions about questions: Inquiries into the cognitive bases of surveys, ed. J.M. Tanur, 15–48. New York: Sage Foundation.

    Google Scholar 

  15. Wright, L. 2001. Critical thinking: An introduction to analytical reading and reasoning. Oxford: Oxford University Press.

    Google Scholar 

  16. Weinfurt, K.P., D.P. Sulmasy, K.A. Schulman, and N.J. Meropol. 2003. Patient expectations of benefit from phase I clinical trials: Linguistic considerations in diagnosing a therapeutic misconception. Theoretical Medicine 24: 329–344.

    Article  Google Scholar 

  17. Gadamer, H. 2003. Truth and Method, 2nd ed. (trans: Weinsheimer J., and D.G. Marshall). New York: Continuum Press.

  18. Chauhan, C. 2007. Denouement: A patient reported observation. Value in Health 10(Suppl. 2): S146–S147.

    Article  Google Scholar 

  19. Revicki, D., R.D. Hays, D. Cella, and J. Sloan. 2008. Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. Journal of Clinical Epidemiology 61: 102–109.

    Article  Google Scholar 

  20. Schunemann, H.J., and G.H. Guyatt. 2005. Commentary—goodbye M(C)ID! Hello MID, where do you come from? Health Services Research 40: 593–597.

    Article  Google Scholar 

  21. FDA. 2009. Guidance for industry on patient-reported outcome measures: Use in medicinal product development to support labeling claims. Federal Register 74: 1–43.

    Google Scholar 

  22. Edgar, A. 1995. Weighting health states and strong evaluation. Bioethics 9: 240–251.

    Article  Google Scholar 

  23. Taylor, C. 1985. What is human agency. In Human agency and language: Philosophical papers, vol. 1, 15–44. Cambridge: Cambridge University Press.

  24. Westerman, M.J., T. Hak, M.A. Sprangers, H.J. Groen, G. van der Wal, and A.M. The. 2008. Listen to their answers! Response behavior in the measurement of physical and role functioning. Quality of Life Research 17: 549–558.

    Article  Google Scholar 

  25. Wyrwich, K.W., and V.M. Tardino. 2006. Understanding global transition assessments. Quality of Life Research 15: 995–1004.

    Article  Google Scholar 

  26. Streiner, D.L., and G.R. Norman. 2003. Health measurement scales: A practical guide to their development and use, 3rd ed. Oxford: Oxford University Press.

    Google Scholar 

  27. McClimans, L. 2010. A theoretical framework for patient-reported outcome measures. Theoretical Medicine and Bioethics 31: 225–240.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Philosophy, University of South Carolina, 901 Sumter Street, Columbia, SC, 29208, USA

    Leah McClimans

Authors
  1. Leah McClimans
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Leah McClimans.

Rights and permissions

Reprints and permissions

About this article

Cite this article

McClimans, L. Interpretability, validity, and the minimum important difference. Theor Med Bioeth 32, 389–401 (2011). https://doi.org/10.1007/s11017-011-9186-9

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11017-011-9186-9

Keywords

Search

Navigation