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Erschienen in:

01.12.2013

Guidelines for secondary analysis in search of response shift

verfasst von: Carolyn E. Schwartz, Sara Ahmed, Richard Sawatzky, Tolulope Sajobi, Nancy Mayo, Joel Finkelstein, Lisa Lix, Mathilde G. E. Verdam, Frans J. Oort, Mirjam A. G. Sprangers

Erschienen in: Quality of Life Research | Ausgabe 10/2013

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Abstract

Objective

Response shift methods have developed substantially in the past decade, with a notable emphasis on model-based methods for response shift detection that are appropriate for the analysis of existing data sets. These secondary data analyses have yielded useful insights and motivated the continued growth of response shift methods. However, there are also challenges inherent to the successful use of secondary analysis for response shift detection. Based on our experience with a number of secondary analyses, we propose guidelines for the optimal implementation of secondary analysis for detecting response shift.

Methods

We review the definition of response shift and recent advances in response shift theory. We describe current statistical methods that have been developed for or applied to response shift detection. We then discuss lessons learned when using these methods to test specific hypotheses about response shift in existing data and of the features of a data set that could guide early decision-making about undertaking a secondary analysis.

Results

A checklist is provided that includes guidelines for secondary analyses focusing on: (1) selecting an appropriate data set to investigate response shift; (2) prerequisites of data sets and their preparation for analysis; (3) managing missing data; (4) confirming that the data fit the requirements and assumptions of the selected response shift detection technique; (5) model fit evaluation; (6) interpreting results/response shift effect sizes; and (7) comparing findings across methods.

Conclusions

The guidelines-checklist has the potential to stimulate rigorous and replicable research using existing data sets and to assist investigators in assessing the appropriateness and potential of a data set and model-based methods for response shift research.

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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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

Jansen, S. J., Stiggelbout, A. M., Nooij, M. A., Noordijk, E. M., & Kievit, J. (2000). Response shift in quality of life measurement in early-stage breast cancer patients undergoing radiotherapy. Quality of Life Research, 9(6), 603–615.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

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.PubMedCrossRef

Ahmed, S., Mayo, N. E., Wood-Dauphinee, S., Hanley, J. A., & Cohen, S. R. (2004). Response shift influenced estimates of change in health-related quality of life poststroke. Journal of Clinical Epidemiology, 57(6), 561–570.PubMedCrossRef

Ahmed, S., Mayo, N. E., Wood-Dauphinee, S., Hanley, J. A., & Cohen, S. R. (2005). The structural equation modeling technique did not show a response shift, contrary to the results of the then test and the individualized approaches. Journal of Clinical Epidemiology, 58(11), 1125–1133.PubMedCrossRef

10.

Wikby, A., Stenstrom, U., Hornquist, J. O., & Andersson, P. O. (1993). Coping behaviour and degree of discrepancy between retrospective and prospective self-ratings of change in quality of life in type 1 diabetes mellitus. Diabetic Medicine, 10(9), 851–854.PubMedCrossRef

11.

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

12.

Ring, L. H. 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.CrossRef

13.

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.PubMedCrossRef

14.

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.CrossRef

15.

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.PubMedCrossRef

16.

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.

17.

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.PubMedCrossRef

18.

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.PubMedCrossRef

19.

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.PubMedCrossRef

20.

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

21.

Finkelstein, J. A., Razmjou, H., & Schwartz, C. E. (2009). Response shift and outcome assessment in orthopedic surgery: Is there is a difference between complete vs. partial treatment? Journal of Clinical Epidemiology, 82, 1189–1190.CrossRef

22.

Schwartz, C. E., 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(9), 1533–1550. doi:10.1007/s11136-006-0025-9.PubMedCrossRef

23.

Oort, F. J., Visser, M. R., & 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.PubMedCrossRef

24.

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.PubMed

25.

Howard, G. S., Ralph, K. M., Gulanick, N. A., Maxwell, S. E., Nance, D. W., & Gerber, S. K. (1979). Internal invalidity in pretest-posttest self-report evaluations and a re-evaluation of retrospective pretests. Applied Psychology Measurement, 3(1), 1–23.CrossRef

26.

Golembiewski, R. T., Billingsley, K., & Yeager, S. (1976). Measuring change and persistence in human affairs: types of change generated by OD designs. J. Applied Behav. Sci., b 12, 133–157.CrossRef

27.

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.PubMedCrossRef

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), 14.PubMedCrossRef

29.

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.PubMedCrossRef

30.

Oort, F. J., Visser, M. R. M., & Sprangers, M. A. G. (2009). Measurement and conceptual perspectives on response shift: Formal definitions of measurement bias, explanation bias, and response shift. Journal of Clinical Epidemiology, 62, 1126–1137.PubMedCrossRef

31.

Korfage, I. J., de Koning, H. J., & Essink-Bot, M. L. (2007). Response shift due to diagnosis and primary treatment of localized prostate cancer: a then-test and a vignette study. Quality of Life Research, 16, 1627–1634.PubMedCrossRef

32.

Sprangers, M., & Hoogstraten, J. (1989). Pretesting effects in retrospective pretest-posttest designs. Journal of Applied Psychology, 74(2), 265–272.CrossRef

33.

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.PubMedCrossRef

34.

Visser, M. R. M., Oort, F. J., & Sprangers, M. A. G. (2005). Methods to detect response shift in quality of life data: A convergent validity study. Quality of Life Research, 14, 629–639.PubMedCrossRef

35.

Schwartz, C. E., & Rapkin, B. D. (2011). Understanding appraisal processes underlying the thentest: A mixed methods investigation. Quality of Life Research, 21(3), 381–388. doi:10.1007/s11136-011-0023-4.

36.

Schwartz, C. E., Sprangers, M. A., Oort, F. J., Ahmed, S., Bode, R., Li, Y., et al. (2011). Response shift in patients with multiple sclerosis: an application of three statistical techniques. Quality of Life Research, 20(10), 1561–1572. doi:10.1007/s11136-011-0056-8.PubMedCrossRef

37.

Howard, G. S., & Dailey, P. R. (1979). Response shift bias: a source of contamination of self-report measures. Journal of Applied Psychology, 64(2), 144–150.CrossRef

38.

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

39.

Li, Y., & Schwartz, C. E. (2011). Data mining for response shift patterns using recursive partitioning tree analysis. Quality of Life Research, 20(10), 1543–1553. doi:10.1007/s11136-011-0004-7.PubMedCrossRef

40.

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.PubMedCrossRef

41.

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.PubMedCrossRef

42.

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.PubMedCrossRef

43.

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.PubMedCrossRef

44.

Li, Y., & Schwartz, C. E. (2011). Using classification and regression tree to examine evidence of quality of life response shift in patients with multiple sclerosis. Quality of Life Research, 20(10), 1543–1553.

45.

Boucekine, M., L’Mouaci, R., Flores, P. M., Butzkueven, H., Baumstarck, K., Ghattas, B., et al. (2012). Understanding response shift in multiple sclerosis patients: application of the Random Forest method. [Abstract]. Quality of Life Research, 21(S1), 32–33.

46.

Lix, L. M., Sajobi, T. T., Sawatzky, R., Liu, J., Mayo, N. E., Huang, Y., et al. (2012). Relative importance measures for reprioritization response shift. Quality of Life Research. doi:10.1007/s11136-012-0198-3.

47.

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

48.

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

49.

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

50.

King-Kallimanis, B. L., Oort, F. J., Visser, M. R., & Sprangers, M. A. (2009). Structural equation modeling of health-related quality-of-life data illustrates the measurement and conceptual perspectives on response shift. Journal of Clinical Epidemiology, 62(11), 1157–1164.PubMedCrossRef

51.

Ahmed, S., Mayo, N. E., Corbiere, M., Wood-Dauphinee, S., Hanley, J., & Cohen, R. (2005). Change in quality of life in people with stroke over time: true change or response shift? Quality of Life Research, 14, 611–627.PubMedCrossRef

52.

Barclay-Goddard, R., Lix, L. M., Tatec, R., Weinberg, L., & Mayo, N. E. (2009). Response shift was identified over multiple occasions with a structural equation modeling framework. Journal of Clinical Epidemiology, 62, 1181–1188.PubMedCrossRef

53.

Nolte, S., Elsworth, G. R., Sinclair, A. J., & Osborne, R. H. (2009). A test of measurement invariance fails to support the application of then-test questions as a remedy to response shift bias. Journal of Clinical Epidemiology, 62, 1173–1180.PubMedCrossRef

54.

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.PubMedCrossRef

55.

King-Kallimanis, B. L., Oort, F. J., Nolte, S., Schwartz, C. E., & Sprangers, M. A. (2011). Using structural equation modeling to detect response shift in performance and health-related quality of life scores of multiple sclerosis patients. Quality of Life Research, 20(10), 1527–1540. doi:10.1007/s11136-010-9844-9.PubMedCrossRef

56.

Ahmed, S., Sawatzky, R., Levesque, J. F., Ehrmann-Feldman, D., & Schwartz, C. E. (2012). Minimal Evidence of Response Shift in the Absence of a Catalyst. Quality of Life Research, 21(Supplement 1), 2–3.

57.

Ahmed, S., Schwartz, C., Ring, L., & Sprangers, M. A. (2009). Applications of health-related quality of life for guiding health care: Advances in response shift research. Journal of Clinical Epidemiology, 62(11), 1115–1117. doi:10.1016/j.jclinepi.2009.04.006.PubMedCrossRef

58.

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.PubMedCrossRef

59.

Jung, T., & Wickrama, K. A. S. (2008). An introduction to latent class growth analysis and growth mixture modeling. Social and Personality Psychology Compass, 2(1), 302–317. doi:10.1111/j.1751-9004.2007.00054.x.CrossRef

60.

Sawatzky, R., Ratner, P. A., Kopec, J. A., & Zumbo, B. D. (2011). Latent variable mixture models: a promising approach for the validation of patient reported outcomes. Quality of Life Research. doi:10.1007/s11136-011-9976-6.

61.

Hancock, G. R., & Samuelsen, K. M. (2008). Advances in latent variable mixture models. Charlotte, NC: Information Age Pub.

62.

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

63.

Goodkin, D. E., Reingold, S., Sibley, W., Wolinsky, J., McFarland, H., Cookfair, D., et al. (1999). Guidelines for clinical trials of new therapeutic agents in multiple sclerosis: Reporting extended results from phase III clinical trials. National Multiple Sclerosis Society Advisory Committee on Clinical Trials of New Agents in Multiple Sclerosis. [Practice Guideline]. Annals of Neurology, 46(1), 132–134.PubMedCrossRef

64.

Goodkin, D. E., Ross, J. S., Medendorp, S. V., Konecsni, J., & Rudick, R. A. (1992). Magnetic resonance imaging lesion enlargement in multiple sclerosis. Disease-related activity, chance occurrence, or measurement artifact? [Research Support, Non-U.S. Gov’t]. Archives of Neurology, 49(3), 261–263.PubMedCrossRef

65.

Deyo, R. A., Battie, M., Beurskens, A. J., Bombardier, C., Croft, P., Koes, B., et al. (1998). Outcome measures for low back pain research. A proposal for standardized use. SPINE, 23(18), 2003–2013.PubMedCrossRef

66.

Finkelstein, J. A., Quaranto, B. R., & Schwartz, C. E. (2013). Threats to the internal validity of spinal surgery outcome assessment: Recalibration response shift or implicit theories of change? Applied Quality of Life Research. (in press).

67.

Sawatzky, R., Gadermann, A., Ratner, P. A., Zumbo, B. D., & Lix, L. (2012). Identifying individuals with inflammatory bowel disease who experienced response shift: A latent class analysis? Quality of Life Research, 21(Supplement 1), 33.

68.

Gandhi, P. K., Ried, L. D., Huang, I. C., Kimberlin, C. L., & Kauf, T. L. (2012). Assessment of response shift using two structural equation modeling techniques. Quality of life research : an international journal of quality of life aspects of treatment, care and rehabilitation. doi:10.1007/s11136-012-0171-1.

69.

Tabachnick, B. G., & Fidell, L. S. (2013). Assumptions and limitations of multivariate statistical methods. In Using multivariate statistics (6th ed.). Boston: Allyn and Bacon.

70.

Fairclough, D. L. (2010). Design and analysis of quality of life studies in clinical trials (2 ed.). Chapman & Hall/CRC Interdisciplinary Statistics Series). New York: CRC Press, Taylor & Francis Group.

71.

Fielding, S., Fayers, P. M., & Ramsay, C. R. (2009). Investigating the missing data mechanism in quality of life outcomes: A comparison of approaches. Health and Quality of Life Outcomes, 7, 57–66.PubMedCrossRef

72.

Little, R. J. A., & Rubin, B. D. (2002). Statistical analysis with missing data (2nd ed.). New Jersey: Wiley.

73.

Ibrahim, J. G., & Molenbergh, G. (2009). Missing data methods in longitudinal studies: A review. Test, 18, 1–43.PubMedCrossRef

74.

Little, R. J., D’Agostino, R., Cohen, M. L., Dickersin, K., Emerson, S. S., Farrar, J. T., et al. (2012). The prevention and treatment of missing data in clinical trials. The New England journal of medicine, 367(14), 1355–1360. doi:10.1056/NEJMsr1203730.PubMedCrossRef

75.

Fan, X., Thompson, B., & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling, 6(1), 56–83. doi:10.1080/10705519909540119.CrossRef

76.

Jackson, D. L. (2003). Revisiting Sample Size and Number of Parameter Estimates: Some Support for the N:q Hypothesis. Structural Equation Modeling: A Multidisciplinary Journal, 10(1), 128–141.CrossRef

77.

Barrett, P. (2007). Structural equation modelling: Adjudging model fit. Personality and Individual Differences, 42, 815–824.CrossRef

78.

Shah, R., & Goldstein, S. M. (2006). Use of structural equation modeling in operations management research: Looking back and forward. Journal of Operations Management, 24, 148–169.CrossRef

79.

Muthén, L., & Muthén, B. (2002). How to use a Monte Carlo study to decide on sample size and determine power. Structural Equation Modeling, 9, 599–620.CrossRef

80.

Steyerberg, E. W., Harrell, F. E. J., Borsboom, G. J. J. M., Eijkemans, M. J. C., Vergouwe, Y., & Habbema, J. D. F. (2001). Internal validation of predictive models: Efficiency of some procedures for logistic regression analysis. Journal of Clinical Epidemiology, 54, 774–781.PubMedCrossRef

81.

Mayo, N. E., Scott, S. C., & Lix, L. (2012). Caring for a spouse with stroke: True change in QOL and response shift. Quality of Life Research, 21(Supplement 1), 2–3.

82.

Brossart, D. F., Clay, D. L., & Willson, V. L. (2002). Methodological and statistical considerations for threats to internal validity in pediatric outcome data: Response shift in self-report outcomes. Journal of Pediatric Psychology, 27(1), 97–107.PubMedCrossRef

83.

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, 20(10), 1555–1560. doi:10.1007/s11136-011-0005-6.PubMedCrossRef

84.

Schwartz, C. E., Sajobi, T., Lix, L., Quaranto, B. R., & Finkelstein, J. A. (2013). Changing values, changing outcomes: The influence of reprioritization response shift on outcome assessment after spine surgery. Quality of Life Research, (in press). doi:10.1007/s11136-013-0377-x.

Titel: Guidelines for secondary analysis in search of response shift
verfasst von: Carolyn E. Schwartz
Sara Ahmed
Richard Sawatzky
Tolulope Sajobi
Nancy Mayo
Joel Finkelstein
Lisa Lix
Mathilde G. E. Verdam
Frans J. Oort
Mirjam A. G. Sprangers
Publikationsdatum: 01.12.2013
Verlag: Springer Netherlands
Erschienen in: Quality of Life Research / Ausgabe 10/2013
Print ISSN: 0962-9343
Elektronische ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-013-0402-0

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Abstract

Objective

Methods

Results

Conclusions

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