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Identifying response shift statistically at the individual level.

Nancy E Mayo1, Susan C Scott, Nandini Dendukuri

  • 1Division of Clinical Epidemiology, McGill University, Royal Victoria Hospital, Montreal, QC, Canada. nancy.mayo@mcgill.ca

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|April 4, 2008
PubMed
Summary
This summary is machine-generated.

This study developed a new method to detect response shift by comparing reported and predicted health over time in stroke patients. The findings suggest this approach can identify individuals whose health perceptions changed unexpectedly.

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Area of Science:

  • Health outcomes research
  • Psychometrics
  • Longitudinal data analysis

Background:

  • Response shift occurs when individuals' internal standards or definitions of health change over time.
  • Identifying response shift is crucial for accurately interpreting longitudinal health data, particularly in chronic conditions like stroke.
  • Existing methods for detecting response shift can be complex and may not capture the nuances of individual changes.

Purpose of the Study:

  • To develop and validate a novel methodology for identifying response shift using longitudinal health data.
  • To assess the feasibility of comparing self-reported health with predicted health trajectories to detect discrepancies.
  • To explore the utility of this approach in a stroke patient cohort.

Main Methods:

  • A response-shift model was created using data from a longitudinal stroke study with health impact measures at multiple time points (baseline, 1, 3, 6, 12 months).
  • Random effects model residuals were used to generate individual health trajectories.
  • The model was validated using existing datasets and simulated data to evaluate its accuracy and robustness against random error.

Main Results:

  • Group-based trajectory analysis revealed seven distinct patterns of health change over 12 months post-stroke.
  • A significant portion of participants (67%) exhibited no discernible response shift.
  • However, 15% of individuals reported lower health than predicted, and 13% reported higher health than predicted, indicating a response shift.

Conclusions:

  • The developed methodology demonstrates promise in identifying response shift in longitudinal health studies.
  • Validation studies support the model's ability to detect changes in health perception.
  • Further research is recommended to compare this method with alternative approaches and predictive models for comprehensive validation.