Skip to main content

Advertisement

Log in

Response shift in patients with multiple sclerosis: an application of three statistical techniques

  • Published:
Quality of Life Research Aims and scope Submit manuscript

Abstract

Objective

With the evolution of theory and methods for detecting recalibration, reprioritization, and reconceptualization response shifts, the time has come to evaluate and compare the current statistical detection techniques. This manuscript presents an overview of a cross-method validation done on the same patient sample.

Methods

Three statistical techniques were used: Structural Equation Modeling, Latent Trajectory Analysis, and Recursive Partitioning and Regression Tree modeling. The study sample (n = 3,008) was drawn from the North American Research Committee on Multiple Sclerosis (NARCOMS) Registry to represent patients soon after diagnosis, classified as having either a self-reported relapsing, progressive, or stable disease trajectory. Patient-reported outcomes included the disease-specific Performance Scales and the Patient-Derived Disease Steps, and the generic SF-12v2 measure.

Results

Small response shift effect sizes were detected by all of the methods. Recalibration response shift was detected by Structural Equation Modeling, Recursive Partitioning Regression Tree demonstrated patterns consistent with all three types of response shift, and Latent Trajectory Analysis, although unable to distinguish types of response shift, did detect response shift in less than 1% of the sample.

Conclusion

The methods and their findings were discussed for operationalization, interpretability, assumptions, ability to use all data points from the study sample, limitations, and strengths. Directions for future research are discussed.

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

Access this article

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

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Sprangers, M. A., & Schwartz, C. E. (1999). Integrating response shift into health-related quality of life research: A theoretical model. Social Science and Medicine, 48(11), 1507–1515.

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  3. Rapkin, B. D., & Schwartz, C. E. (2004). Toward a theoretical model of quality-of-life appraisal: Implications of findings from studies of response shift. Health and Quality of Life Outcomes, 2(1), 14.

    Google Scholar 

  4. Becker, H., Stuifbergen, A., Rogers, S., & Timmerman, G. (2000). Goal attainment scaling to measure individual change in intervention studies. Nursing Research, 49(3), 176–180.

    Article  PubMed  CAS  Google Scholar 

  5. Evers, K. J., & Karnilowicz, W. (1996). Patient attitude as a function of disease state in multiple sclerosis. Social Science and Medicine, 43(8), 1245–1251.

    Article  PubMed  CAS  Google Scholar 

  6. Paterson, B. L. (2003). The koala has claws: applications of the shifting perspectives model in research of chronic illness. Qualitative Health Research, 13(7), 987–994.

    Article  PubMed  Google Scholar 

  7. Schwartz, C. E., & Sendor, M. (1999). Helping others helps oneself: response shift effects in peer support. Social Science and Medicine, 48(11), 1563–1575.

    Article  PubMed  CAS  Google Scholar 

  8. Bernhard, J., Hurny, C., Maibach, R., Herrmann, R., & Laffer, U. (1999). Quality of life as subjective experience: Reframing of perception in patients with colon cancer undergoing radical resection with or without adjuvant chemotherapy. Swiss group for clinical cancer research (SAKK). Annals of Oncology, 10(7), 775–782.

    Article  PubMed  CAS  Google Scholar 

  9. Bernhard, J., Lowy, A., Maibach, R., & Hürny, C. (2001). Response shift in the perception of health for utility evaluation. An explorative investigation. European Journal of Cancer, 37(14), 1729–1735.

    Article  PubMed  CAS  Google Scholar 

  10. Boyd, N. F., Sutherland, H. J., Heasman, K. Z., Tritchler, D. L., & Cummings, B. J. (1990). Whose utilities for decision analysis? Medical Decision Making, 10(1), 58–67.

    Article  PubMed  CAS  Google Scholar 

  11. Breetvelt, I. S., & Van Dam, F. S. (1991). Underreporting by cancer patients: The case of response-shift. Social Science & Medicine, 32(9), 981–987.

    Google Scholar 

  12. Cella, D., Hahn, E. A., & Dineen, K. (2002). Meaningful change in cancer-specific quality of life scores: Differences between improvement and worsening. Quality of Life Research, 11(3), 207–221.

    Article  PubMed  Google Scholar 

  13. Chapman, G. B., Elstein, A. S., Kuzel, T. M., Sharifi, R., Nadler, R. B., Andrews, A., et al. (1998). Prostate cancer patients’ utilities for health states: How it looks depends on where you stand. Medical Decision Making, 18(3), 278–286.

    Article  PubMed  CAS  Google Scholar 

  14. Hagedoorn, M., Sneeuw, K. C., & Aaronson, N. K. (2002). Changes in physical functioning and quality of life in patients with cancer: Response shift and relative evaluation of one’s condition. Journal of Clinical Epidemiology, 55(2), 176–183.

    Article  PubMed  Google Scholar 

  15. Jansen, S. J. T., Stiggelbout, A. M., Nooij, M. A., Noordijk, E. M., & Kievit, J. (2001). Response shift in quality of life measurement in early-stage breast cancer patients undergoing radiotherapy. Quality of Life Research, 9, 603–615.

    Google Scholar 

  16. Kagawa-Singer, M. (1993). Redefining health: Living with cancer. Social Science and Medicine, 37(3), 295–304.

    Article  PubMed  CAS  Google Scholar 

  17. Oort, F. J., Visser, M. R. M., & Sprangers, M. A. G. (2005). An application of structural equation modeling to detect response shifts and true change in quality of life data from cancer patients undergoing invasive surgery. Quality of Life Research, 14, 599–609.

    Google Scholar 

  18. Rees, J., Waldron, D., O’Boyle, C., Ewings, P., & MacDonagh, R. (2005). The measurement of response shift in patients with advanced prostate cancer and their partners. Health and Quality of Life Outcomes, 3(21), 1–8.

    Google Scholar 

  19. Schwartz, C. E., Feinberg, R. G., Jilinskaia, E., & Applegate, J. C. (1999). An evaluation of a psychosocial intervention for survivors of childhood cancer: Paradoxical effects of response shift over time. Psychooncology, 8(4), 344–354.

    Article  PubMed  CAS  Google Scholar 

  20. Sprangers, M. A., Van Dam, F. S., Broersen, J., Lodder, L., Wever, L., Visser, M. R., et al. (1999). Revealing response shift in longitudinal research on fatigue–the use of the thentest approach. Acta Oncologica, 38(6), 709–718.

    Article  PubMed  CAS  Google Scholar 

  21. Tederous-Williams, M. (2003). Response shift in women who have been pregnant with cancer. Quality of LIfe Research, 12(7), 783.

    Google Scholar 

  22. Ahmed, S., Mayo, N. E., Wood-Dauphinee, S., & Hanley, J. (2001). Response shift in the assessment of health-related quality of life (HRQL) post-stroke. Quality of Life Research, 10, 204.

    Google Scholar 

  23. Ahmed, S., Mayo, N., Wood-Dauphinee, S., Hanley, J., & Cohen, R. (2005). Using the patient generated index to evaluate response shift post-stroke. Quality of Life Research, 14, 2247–2257.

    Article  PubMed  Google Scholar 

  24. Ahmed, S. (2004). Response shift, health-related quality of life post-stroke (pp. 1–235). Montreal: Department of Epidemiology and Biostatistics, McGill University.

    Google Scholar 

  25. Postulart, D., & Adang, E. M. (2000). Response shift and adaptation in chronically ill patients. Medical Decision Making, 20(2), 186–193.

    Article  PubMed  CAS  Google Scholar 

  26. Wikby, A., Stenström, U., Hörnquist, J. O., & Andersson, P. O. (1993) Coping behavior and degree of discrepancy between retrospective and prospective self-ratings of change in quality of life in Type 1 diabetes mellitus. Diabetic Medicine, 10, 851–854.

    Google Scholar 

  27. Daltroy, L. H., Larson, M. G., Eaton, H. M., Phillips, C. B., & Liang, M. H. (1999). Discrepancies between self-reported and observed physical function in the elderly: The influence of response shift and other factors. Social Science and Medicine, 48(11), 1549–1561.

    Article  PubMed  CAS  Google Scholar 

  28. Heidrich, S. M., & Ryff, C. D. (1993). The role of social comparisons processes in the psychological adaptation of elderly adults. Journal of Gerontology, 48(3), 127–136.

    Google Scholar 

  29. Rijken, M., Komproe, I. H., Ros, W. J. G., Winnubst, J. A. M., & van Heesch, N. C. A. (1995). Subjective well-being of elderly women: Conceptual differences between cancer patients, women suffering from chronic ailments and healthy women. British Journal of Clinical Psychology, 34, 289–300.

    Google Scholar 

  30. Rees, J., MacDonagh, R., Waldron, D., & O’Boyle, C. (2004). Measuring quality of life in patients with advanced cancer. European Journal of Palliative Care, 11(3), 104–106.

    Google Scholar 

  31. Schwartz, C. E., Merriman, M., Reed, G., & Hammes, B. (2004). Measuring patient treatment preferences in end-of-life care research: Applications for advance care planning interventions and response shift research. Journal of Palliative Medicine, 7(2), 233–245.

    Article  PubMed  Google Scholar 

  32. Schwartz, C. E., Wheeler, H. B., Hammes, B., Basque, N., Edmunds, J., Reed, G., et al. (2002). Early intervention in planning end-of-life care with ambulatory geriatric patients: Results of a pilot trial. Archives of Internal Medicine, 162(14), 1611–1618.

    Article  PubMed  Google Scholar 

  33. Schwartz, C. E., Merriman, M. P., Reed, G., & Byock, I. (2005). Evaluation of the Missoula-VITAS quality of life index—Revised: Research tool or clinical tool? Journal of Palliative Medicine, 8(1), 121–135.

    Article  PubMed  Google Scholar 

  34. Ring, L., Hofer, S., Heuston, F., Harris, D., O’Boyle, C. A. (2005). Response shift masks the treatment impact on patient reported outcomes (PROs): The example of individual quality of life in edentulous patients. Health & Quality of Life Outcomes, 3, 55.

  35. Razmjou, H., Yee, A., Ford, M., & Finkelstein, J. (2006). Response shift in outcome assessment in patients undergoing total knee arthroplasty. Journal of Bone & Joint Surgery—American Volume, 88(12), 2590–2595.

    Article  Google Scholar 

  36. Schwartz, C., Bode, R., Repucci, N., Becker, J., Sprangers, M. A., & Fayers, P. M. (2006). The clinical significance of adaptation to changing health: A meta-analysis of response shift. Quality of Life Research, 15, 1533–1550.

    Google Scholar 

  37. Ahmed, S., Bourbeau, J., Maltais, F., & Mansour, A. (2009). The Oort structural equation modeling approach detected a response shift after a COPD self-management program not detected by the Schmitt technique. Journal of Clinical Epidemiology, 62, 1165–1172.

    Article  PubMed  Google Scholar 

  38. Mayo, N., Scott, C., & Ahmed, S. (2009). Case management post-stroke did not induce response shift: The value of residuals. Journal of Clinical Epidemiology, 62, 1148–1156.

    Google Scholar 

  39. Li, Y., & Rapkin, B. (2009). Classification and regression tree analysis to identify complex cognitive paths underlying quality of life response shifts: A study of individuals living with HIV/AIDS. Journal of Clinical Epidemiology, 62, 1138–1147.

    Article  PubMed  Google Scholar 

  40. Ring, L., Hofer, S., Heuston, F., Harris, D., & O’Boyle, C. A. (2005). Response shift masks the treatment impact on patient reported outcomes (PROs): The example of individual quality of life in edentulous patients. Health and Quality of Life Outcomes, 3, 55.

  41. Schmitt, N. (1982). The use of analysis of covariance structures to assess beta and gamma change. Multivariate Behavioral Research, 17, 343–358.

    Google Scholar 

  42. Shimozuma, K., Imai, H., Kuroi, K., Ohsumi, S., & Ono, M. (2007). Recent topics of health outcomes research in oncology. Breast Cancer, 14(1), 60–65.

    Article  PubMed  Google Scholar 

  43. Ahmed, S., Mayo, N., Scott, S., Kuspinar, A., & Schwartz, C. (2011). Using latent trajectory analysis of residuals to detect response shift in general health among patients with multiple sclerosis article. Quality of Life Research. doi:10.1007/s11136-011-0005-6.

  44. King-Kallimanis, B. L., Oort, F. J., Nolte, S., Schwartz, C. E., & Sprangers, M. A. G. (2011). Using structural equation modeling to detect response shift in performance and quality of life scores of multiple sclerosis patients. Quality of Life Research. doi:10.1007/s11136-010-9844-9.

  45. Li, Y., & Schwartz, C. E. (2011). Data mining for response shift patterns in multiple sclerosis patients using recursive partitioning tree analysis. Quality of Life Research. doi:10.1007/s11136-011-0004-7.

  46. NMSS. (2005). Multiple sclerosis information sourcebook. New York, NY: Information Resource Center and Library of the National Multiple Sclerosis Society.

    Google Scholar 

  47. Trapp, B. D., & Nave, K.-A. (2008). Multiple sclerosis: An immune or neurodegenerative disorder? Annual Review of Neuroscience, 31, 247–269.

    Google Scholar 

  48. McFarland, H. F., & Martin, R. (2007). Multiple sclerosis: A complicated picture of autoimmunity. Nature Immunology, 8, 913–919.

    Google Scholar 

  49. Dutta, R., & Trapp, B. D. (2007). Pathogenesis of axonal and neuronal damage in multiple sclerosis. Neurology, 68(Suppl 3), S22–S31.

    Article  PubMed  Google Scholar 

  50. Filippini, G., Munari, L., Incorvaia, B., Ebers, G. C., Polman, C., D’Amico, R., et al. (2003). Interferons in relapsing remitting multiple sclerosis: A systematic review. Lancet, 361, 545–552.

    Article  PubMed  CAS  Google Scholar 

  51. Rice, G. P., Incorvaia, B., Munari, L., Ebers, G., Polman, C., D’Amico, R., et al. (2001) Interferon in relapsing-remitting multiple sclerosis. Cochrane Database of Systematic Reviews, 4, CD002002.

  52. Snook, E. M., & Motl, R. W. (2009). Effect of exercise training on walking mobility in multiple sclerosis: A meta-analysis. Neurorehabilitation and Neural Repair (in press).

  53. Tremlett, H., Zhao, Y., Rieckmann, P., & Hutchinson, M. (2010). New perspectives in the natural history of multiple sclerosis. Neurology, 74, 2004–2015.

    Article  PubMed  Google Scholar 

  54. Ware, J., Jr., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey: Construction of scales and preliminary tests of reliability and validity. Medical Care, 34(3), 220–233.

    Google Scholar 

  55. Schwartz, C. E., Vollmer, T., & Lee, H. (1999). Reliability and validity of two self-report measures of impairment and disability for MS. North American research consortium on multiple sclerosis outcomes study group. Neurology, 52(1), 63–70.

    PubMed  CAS  Google Scholar 

  56. Hohol, M. J., Orav, E. J., & Weiner, H. L. (1995). Disease steps in multiple sclerosis: A simple approach to evaluate disease progression. Neurology, 45, 251–255.

    Google Scholar 

  57. Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33(11), 1444–1452.

    PubMed  CAS  Google Scholar 

  58. Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Hillsdale, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  59. Bollen, K. A. (1989). Structural equations with latent variables. New York: Wiley.

  60. Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14, 587–598.

    Google Scholar 

  61. Bryk, A. S., & Raudenbush, S. W. (1992). Hierarchical linear models: Applications, data analysis methods. Thousand Oaks, CA: Sage Publications.

    Google Scholar 

  62. Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1993). Classification and regression trees. New York: Chapman and Hall/CRC.

  63. Martin, M. A., Meyricke, R., O’Neill, T., & Roberts, S. (2006). Mastectomy or breast conserving surgery? Factors affecting type of surgical treatment for breast cancerda classification tree approach. BMC Cancer, 6, 98.

  64. Gruenewald, T. L., Mroczek, D. K., Ryff, C. D., & Singer, B. H. (2008) Diverse pathways to positive and negative affect in adulthood and later life: An integrative approach using recursive partitioning. Developmental Psychology, 44, 330–343.

    Google Scholar 

  65. Radespiel-Troger, M., Rabenstein, T., Schneider, H. T., & Lausen, B. (2003). Comparison of tree-based methods for prognostic stratification of survival data. Artificial Intelligence in Medicine, 28, 323–341.

    Article  PubMed  CAS  Google Scholar 

  66. Sedrakyan, A., Zhang, H., Treasure, T., & Krumholz, H. M. (2006). Recursive partitioning-based preoperative risk stratification for atrial fibrillation after coronary artery bypass surgery. American Heart Journal, 151, 720–724.

    Google Scholar 

  67. Norman, G. (2003). Hi! How are you? Response shift, implicit theories and differing epistemologies. Quality of Life Research, 12(3), 239–249.

    Article  PubMed  Google Scholar 

  68. Schwartz, C. E., & Rapkin, B. D. (2004). Reconsidering the psychometrics of quality of life assessment in light of response shift and appraisal. Health and Quality of Life Outcomes, 2, 16.

Download references

Acknowledgments

This work was funded in part by a NARCOMS Visiting Scientist Fellowship to Dr. Schwartz, which was supported through a Foundation of the Consortium of Multiple Sclerosis Centers grant from EMD Serono, Inc. NARCOMS is supported by the Consortium of Multiples Sclerosis Centers and its Foundation. Dr. Li was supported in part by a Weill Cornell Medical College Clinical and Translational Science Award (NIH UL1-RR024996) (PI: Julianne Imperato-McGinley MD).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carolyn E. Schwartz.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Schwartz, C.E., Sprangers, M.A.G., Oort, F.J. et al. Response shift in patients with multiple sclerosis: an application of three statistical techniques. Qual Life Res 20, 1561–1572 (2011). https://doi.org/10.1007/s11136-011-0056-8

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-011-0056-8

Keywords

Navigation