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Related Experiment Videos

Using structural equation modeling to detect response shifts and true change.

Frans J Oort1

  • 1Department of Medical Psychology, Academic Medical Centre, University of Amsterdam, The Netherlands. f.j.oort@amc.uva.nl

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|July 19, 2005
PubMed
Summary

Patient-reported outcome changes can be misleading due to response shifts. Structural equation modeling helps identify these shifts and measure true patient-reported outcome changes accurately.

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Area of Science:

  • Psychometrics
  • Health Outcomes Research
  • Statistical Modeling

Background:

  • Assessing patient-reported outcomes (PROs) is crucial in healthcare.
  • Changes in PROs can be influenced by various factors beyond genuine health status shifts.
  • Different types of response shift, including recalibration, reprioritization, and reconceptualization, complicate the interpretation of PRO data.

Purpose of the Study:

  • To address the challenge of accurately assessing change in patient-reported outcomes.
  • To introduce and explain the utility of structural equation modeling (SEM) for analyzing PRO data.
  • To differentiate between true change and various forms of response shift in patient assessments.

Main Methods:

  • Utilizing structural equation modeling (SEM) as a statistical framework.

Related Experiment Videos

  • Applying SEM to detect and quantify different types of response shift (recalibration, reprioritization, reconceptualization).
  • Demonstrating how SEM can isolate and measure 'true' change in patient-reported outcomes.

    • Main Results:

    • Structural equation modeling provides a robust method for identifying response shifts in PROs.
    • SEM allows for the disentanglement of true change from response shift phenomena.
    • The proposed SEM approach enhances the validity of longitudinal PRO assessments.

    Conclusions:

    • Response shifts significantly impact the interpretation of patient-reported outcomes.
    • Structural equation modeling is a powerful tool for addressing response shifts in PRO research.
    • Accurate measurement of true change in PROs is achievable with appropriate statistical methodologies like SEM.