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Measuring individual true change with PROMIS using IRT-based plausible values.

Emily H Ho1, Jay Verkuilen2, Felix Fischer3,4

  • 1Feinberg School of Medicine, Department of Medical Social Sciences, Northwestern University, Chicago, IL, USA. emily-ho@northwestern.edu.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|October 25, 2022
PubMed
Summary

This study used plausible values to analyze true within-individual change in COPD patients, finding that computer-adaptive tests better detect changes than short forms. This method accounts for measurement error in patient-reported outcomes.

Keywords:
COPDChronic illnessItem response theoryMeaningful changePROMISPlausible values

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Area of Science:

  • Measurement science
  • Psychometrics
  • Health outcomes research

Background:

  • Item Response Theory (IRT)-based patient-reported outcome measures (PROMs) like PROMIS provide standard errors, crucial for tracking individual patient changes over time.
  • Acknowledging and accounting for measurement error is vital for accurate interpretation of longitudinal PROM data, especially in chronic conditions like COPD.

Purpose of the Study:

  • To utilize plausible values for accounting for measurement error in IRT-based PROMs.
  • To analyze the probability of true within-individual change in stable and exacerbated COPD patients.
  • To compare the change detection capabilities of PROMIS Short Forms and Computer Adaptive Tests (CATs).

Main Methods:

  • A longitudinal, observational study involving 185 stable and exacerbated COPD patients over 3 months.
  • PROMIS Physical Function and Fatigue T-scores were collected, with 1000 plausible values imputed from posterior distributions at each measurement.
  • Probability of true change was calculated using thresholds like the minimally important difference.

Main Results:

  • With 95% certainty, 47.5% of exacerbated COPD patients reported reduced fatigue versus 26.5% of stable patients.
  • PROMIS CATs demonstrated a superior ability to detect change compared to PROMIS Short Forms.
  • The method illustrated individual probabilities of change across different time points.

Conclusions:

  • Plausible values provide a flexible approach to incorporate measurement error in analyses at both individual and sample levels.
  • Assessing the probability of true change complements existing methods and enhances the interpretation of patient outcomes.
  • This approach facilitates a more nuanced understanding of treatment effects and disease progression in clinical practice.