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Quality of Life Research

, Volume 20, Issue 10, pp 1543–1553 | Cite as

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

  • Yuelin Li
  • Carolyn E. SchwartzEmail author
Article

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.

Keywords

Response shift Statistical analysis Recursive and partitioning tree analysis Methods Multiple sclerosis Appraisal 

Notes

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.

References

  1. 1.
    Shawa, M. J., et al. (2001). Knowledge management and data mining for marketing. Decision Support Systems, 31(1), 127–137.CrossRefGoogle Scholar
  2. 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. 3.
    Koh, H. C., & Tan, G. (2005). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 64–72.PubMedGoogle Scholar
  4. 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.PubMedGoogle Scholar
  5. 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.PubMedCrossRefGoogle Scholar
  6. 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.Google Scholar
  7. 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.Google Scholar
  8. 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.PubMedCrossRefGoogle Scholar
  9. 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.Google Scholar
  10. 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.PubMedCrossRefGoogle Scholar
  11. 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.PubMedCrossRefGoogle Scholar
  12. 12.
    NMSS. (2005). Multiple sclerosis information sourcebook. New York, NY: Information Resource Center and Library of the National Multiple Sclerosis Society.Google Scholar
  13. 13.
    Tremlett, H., et al. (2010). New perspectives in the natural history of multiple sclerosis. Neurology, 74, 2004–2015.PubMedCrossRefGoogle Scholar
  14. 14.
    Trapp, B. D., & Nave, K.-A. (2008). Multiple sclerosis: An immune or neurodegenerative disorder? Annual Review of Neuroscience, 31, pp. 247–269.Google Scholar
  15. 15.
    Tintore, M. (2009). New options for early treatment of multiple sclerosis. Journal of the Neurological Sciences, 277(Suppl 1), S9–S11.PubMedCrossRefGoogle Scholar
  16. 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.PubMedCrossRefGoogle Scholar
  17. 17.
    Torjano, M., et al. (2007). New natural history of interferon-beta-treated relapsing multiple sclerosis. Annals of Neurology, 61(4), 300–306.CrossRefGoogle Scholar
  18. 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.PubMedGoogle Scholar
  19. 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.Google Scholar
  20. 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.Google Scholar
  21. 21.
    Cella, D. F., et al. (1996). Validation of the functional assessment of multiple sclerosis quality of life instrument. Neurology, 47, pp. 129–139.Google Scholar
  22. 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.Google Scholar
  23. 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.Google Scholar
  24. 24.
    Filippini, G., et al. (2003). Interferons in relapsing remitting multiple sclerosis: A systematic review. Lancet, 361, 545–552.PubMedCrossRefGoogle Scholar
  25. 25.
    Rice, G. P., et al. (2001). Interferon in relapsing-remitting multiple sclerosis. Cochrane Database of Systematic Reviews, 4, p. CD002002.Google Scholar
  26. 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).Google Scholar
  27. 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.PubMedCrossRefGoogle Scholar
  28. 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. 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.PubMedCrossRefGoogle Scholar
  30. 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.Google Scholar
  31. 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. 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.Google Scholar
  33. 33.
    Kurtzke, J. F. (1983). Rating neurologic impairment in multiple sclerosis: An expanded disability status scale (EDSS). Neurology, 33(11), 1444–1452.PubMedGoogle Scholar
  34. 34.
    Ware, J. E., Jr., Kosinski, M., & Keller, S. D. (1996). A 12-item short-form health survey. Medical Care, 34(3), 220–233.PubMedCrossRefGoogle Scholar
  35. 35.
    Therneau, T. M., & Atkinson, E. J. (1997). An introduction to recursive partitioning using the Rpart routines. Rochester, MN: Mayo Foundation.Google Scholar
  36. 36.
    Venables, W. N., & Ripley, B. D. (2002). Modern applied statistics with S. New York: Springer Science & Business Media.Google Scholar
  37. 37.
    Team, R. D. C. (2008). R: a language, environment for statistical computing. Vienna, Austria: The R Foundation for Statistical Computing.Google Scholar
  38. 38.
    Abdolell, M., et al. (2002). Binary partitioning for continuous longitudinal data: Categorizing a prognostic variable. Statistics in Medicine, 21, 3395–3409.PubMedCrossRefGoogle Scholar
  39. 39.
    Venables, W. N., & Ripley, B. D. (1999). Modern applies statistics with S, 4th ed. Statistics and Computing. New York: Springer.Google Scholar
  40. 40.
    Ware, J. E., Jr. (1995). The status of health assessment 1994. Annual Review of Public Health, 16, pp. 327–354.Google Scholar
  41. 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).Google Scholar
  42. 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).Google Scholar
  43. 43.
    Oort, F. J. (2005). Using structural equation modeling to detect response shifts and true change. Quality of Life Research, 14, pp. 587–598.Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  1. 1.Behavioral Science, Memorial Sloan Kettering Cancer CenterNew YorkUSA
  2. 2.DeltaQuest Foundation, Inc.ConcordUSA
  3. 3.Departments of Medicine and Orthopaedic SurgeryTufts University School of MedicineBostonUSA

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