Skip to main content

Advertisement

SpringerLink
Log in

Data mining for response shift patterns in multiple sclerosis patients using recursive partitioning tree analysis

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

An Erratum to this article was published on 21 December 2011

Abstract

Aims

To examine evidence of QOL response shift in patients with multiple sclerosis (MS) using recursive partitioning tree analysis (RPART) technique.

Methods

Subjects: MS patients from the NARCOMS registry assessed an average of 6 times at a median interval of 6 months. Outcomes: SF-12v2 Physical & Mental Component Scores (PCS, MCS). Covariates: Patient-determined disease steps, Performance Scales, and symptomatic therapies. RPART trees were fitted separately by 3 disease-trajectory groups: (1) relapsing (n = 1,582); (2) stable (n = 787); and (3) progressive (n = 639). The resulting trees were interpreted by identifying salient terminal nodes that showed the unexpected quantitative patterns of contrasting MCS and PCS scores (e.g., PCS deteriorates but MCS is stable or improves), using a minimally important difference of at least 5 points on the SF-12v2. Qualitative indicators of response shift were different thresholds (recalibration), content (reconceptualization), and order (reprioritization) of disability domains in predicting PCS change by group.

Results

Overall, 20% of patients demonstrated response shift quantitatively, with 10% in the “progressive” cohort, 8% in the “relapsing” cohort, and 2% in the “stable” cohort. RPART trees differed qualitatively across disease-trajectory groups in patterns suggestive of recalibration, reprioritization, and reconceptualization. Disability subscales, but not symptom management, distinguished homogenous groups.

Conclusions

PCS and MCS change scores are obfuscated by response shifts. The contingent true scores for PCS change scores are not comparable across patient groups.

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.

Institutional subscriptions

Figs. 1–3

Similar content being viewed by others

References

  1. Shawa, M. J., et al. (2001). Knowledge management and data mining for marketing. Decision Support Systems, 31(1), 127–137.

    Article  Google Scholar 

  2. Sung, T. K., Chang, N., & Lee, G. (1999). Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems, 16(1), 63–85.

    Google Scholar 

  3. Koh, H. C., & Tan, G. (2005). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 64–72.

    PubMed  Google Scholar 

  4. Koh, H. C., & Leong, S. K. (2001). Data mining applications in the context of casemix. Annals of the Academy of Medicine, Singapore, 30(4 Suppl), 41–49.

    PubMed  CAS  Google Scholar 

  5. 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 

  6. Martin, M. A., et al. (2006). Mastectomy or breast conserving surgery? Factors affecting type of surgical treatment for breast cancerda classification tree approach. BMC Cancer, 6, p. 98.

  7. Gruenewald, T. L., et al. (2008). Diverse pathways to positive and negative affect in adulthood and later life: An integrative approach using recursive partitioning. Developmental Psychology, 44, pp. 330–343.

  8. Radespiel-Troger, M., et al. (2003). Comparison of tree-based methods for prognostic stratification of survival data. Artificial Intelligence in Medicine, 28, 323–341.

    Article  PubMed  CAS  Google Scholar 

  9. Sedrakyan, A., et al. (2006). Recursive partitioning-based preoperative risk stratification for atrial fibrillation after coronary artery bypass surgery. American Heart Journal, 151, pp. 720–724.

  10. Ring, L., Gross, C. R., & McColl, E. (2010). Putting the text back into context: Toward increased use of mixed methods for qualitative research. Quality of Life Research, 19(5), 613–615.

    Article  PubMed  Google Scholar 

  11. Brod, M., Tesler, L. E., & Christensen, T. L. (2009). Qualitative research and content validity: Developing best practices based on science and experience. Quality of Life Research, 18(9), 1263–1278.

    Article  PubMed  Google Scholar 

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

    Google Scholar 

  13. Tremlett, H., et al. (2010). New perspectives in the natural history of multiple sclerosis. Neurology, 74, 2004–2015.

    Article  PubMed  Google Scholar 

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

  15. Tintore, M. (2009). New options for early treatment of multiple sclerosis. Journal of the Neurological Sciences, 277(Suppl 1), S9–S11.

    Article  PubMed  CAS  Google Scholar 

  16. Brown, M. G., et al. (2007). How effective are disease-modifying drugs in delaying progression in relapsing-onset MS? Neurology, 69(15), 1498–1507.

    Article  PubMed  CAS  Google Scholar 

  17. Torjano, M., et al. (2007). New natural history of interferon-beta-treated relapsing multiple sclerosis. Annals of Neurology, 61(4), 300–306.

    Article  Google Scholar 

  18. 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 

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

  20. Vickrey, B. G., et al. (1995). A health-related quality of life measure for multiple sclerosis. Quality of Life Research, 4, pp. 187–206.

  21. Cella, D. F., et al. (1996). Validation of the functional assessment of multiple sclerosis quality of life instrument. Neurology, 47, pp. 129–139.

  22. Hamilton, B. B., et al. (1987). A uniform data system for medical rehabilitation. In M. J. Fuhrer (Ed.), Rehabilitation outcomes: An analysis and measurements. Baltimore: Brooks, pp. 137–147.

  23. Fisher, J. S., et al. (1999). Recent developments in the assessment of quality of life in multiple sclerosis. Multiple Sclerosis, 5, pp. 251–259.

  24. Filippini, G., et al. (2003). Interferons in relapsing remitting multiple sclerosis: A systematic review. Lancet, 361, 545–552.

    Article  PubMed  CAS  Google Scholar 

  25. Rice, G. P., et al. (2001). Interferon in relapsing-remitting multiple sclerosis. Cochrane Database of Systematic Reviews, 4, p. CD002002.

  26. 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).

  27. Sprangers, M. A. G., & 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 

  28. 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), p. 14.

    Google Scholar 

  29. McHorney, C. A., Ware, J. E., Jr., & Raczek, A. E. (1993). 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(3), 247–263.

    Article  PubMed  CAS  Google Scholar 

  30. 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, p. 16.

  31. Sackett, D. L., & Torrance, G. W. (1978). The utility of different health states as perceived by the general public. Journal of Chronic Disease, 31, pp. 697–704.

    Google Scholar 

  32. Ubel, P. A., Loewenstein, G. & Jepson, C. (2003). Whose quality of life? A commentary on exploring discrepancies between health state evaluations of patients and the general public. Quality of Life Research, 12, pp. 599–607.

  33. 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 

  34. Ware, J. E., Jr., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey. Medical Care, 34(3), 220–233.

    Article  PubMed  Google Scholar 

  35. Therneau, T. M., & Atkinson, E. J. (1997). An introduction to recursive partitioning using the Rpart routines. Rochester, MN: Mayo Foundation.

    Google Scholar 

  36. Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S. New York: Springer Science & Business Media.

  37. Team, R. D. C. (2008). R: a language, environment for statistical computing. Vienna, Austria: The R Foundation for Statistical Computing.

    Google Scholar 

  38. Abdolell, M., et al. (2002). Binary partitioning for continuous longitudinal data: Categorizing a prognostic variable. Statistics in Medicine, 21, 3395–3409.

    Article  PubMed  CAS  Google Scholar 

  39. Venables, W. N., & Ripley, B. D. (1999). Modern applies statistics with S, 4th ed. Statistics and Computing. New York: Springer.

  40. Ware, J. E., Jr. (1995). The status of health assessment 1994. Annual Review of Public Health, 16, pp. 327–354.

  41. King-Kallimanis, B. L., et al. (2011). Using structural equation modeling to detect response shift in performance and quality of life scores of multiple sclerosis patients. Quality of Life Research (in press).

  42. Ahmed, S., et al. (2011). Using latent trajectory analysis to detect response shift in general health among multiple sclerosis patients. Quality of Life Research (in review).

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

Download references

Acknowledgments

This work was funded in part by a Visiting Scientist Fellowship from the Consortium of Multiple Sclerosis Centers to Dr. Schwartz. 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). We are grateful to Timothy Vollmer, M.D., for helpful discussions related to classifying patients by disease course, and to Brian Quaranto for assistance in generating figures for manuscript preparation.

Author information

Authors and Affiliations

  1. Behavioral Science, Memorial Sloan Kettering Cancer Center, New York, NY, USA

    Yuelin Li

  2. DeltaQuest Foundation, Inc., Concord, MA, USA

    Carolyn E. Schwartz

  3. Departments of Medicine and Orthopaedic Surgery, Tufts University School of Medicine, Boston, MA, USA

    Carolyn E. Schwartz

Authors
  1. Yuelin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Carolyn E. Schwartz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Carolyn E. Schwartz.

Additional information

The authors Yuelin Li and Carolyn E. Schwartz contributed equally to this work.

An erratum to this article can be found at http://dx.doi.org/10.1007/s11136-011-0092-4

Rights and permissions

Reprints and permissions

About this article

Cite this article

Li, Y., Schwartz, C.E. Data mining for response shift patterns in multiple sclerosis patients using recursive partitioning tree analysis. Qual Life Res 20, 1543–1553 (2011). https://doi.org/10.1007/s11136-011-0004-7

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11136-011-0004-7

Keywords

Search

Navigation