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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Estimating anchor-based minimal important change using longitudinal confirmatory factor analysis.

Berend Terluin1,2, Andrew Trigg3, Piper Fromy4

  • 1Department of General Practice, Amsterdam UMC, Vrije Universiteit Amsterdam, de Boelelaan 1117, 1081 HV, Amsterdam, The Netherlands. b.terluin@amsterdamumc.nl.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|December 27, 2023
PubMed
Summary

Longitudinal confirmatory factor analysis (LCFA) accurately estimates the minimal important change (MIC) in patient-reported outcome measures (PROMs). This method is efficient and reliable, though larger sample sizes (over 125) are recommended for optimal precision.

Keywords:
Longitudinal confirmatory factor analysisLongitudinal item response theoryMeaningful change thresholdMinimal important changePatient-reported outcome measureTransition ratings

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Area of Science:

  • Psychometrics
  • Health Outcomes Research
  • Statistical Modeling

Background:

  • The minimal important change (MIC) quantifies the smallest change in a patient-reported outcome measure (PROM) that holds significance for patients.
  • Accurate estimation of MIC is crucial for interpreting clinical trial results and guiding patient care.
  • Existing methods for MIC estimation have limitations, necessitating the development of more efficient and reliable approaches.

Purpose of the Study:

  • To introduce and evaluate a novel method for estimating MIC using longitudinal confirmatory factor analysis (LCFA).
  • To compare the performance of the LCFA-based MIC estimation method against a longitudinal item response theory (LIRT) approach.
  • To investigate the impact of sample size on the accuracy and precision of LCFA-based MIC estimates.

Main Methods:

  • Simulated 108 datasets with varying characteristics to estimate MIC using both LCFA and LIRT methods.
  • Applied LCFA and LIRT methods to real-world PROMIS Pain Behavior data from 909 patients.
  • Conducted further simulations with 3888 samples across different sizes (125-1000) to assess sample size effects on LCFA-based MIC.

Main Results:

  • LCFA demonstrated comparable accuracy to LIRT in MIC recovery but was over 50 times faster computationally.
  • In PROMIS Pain Behavior data, LCFA yielded an MIC of 2.85, while LIRT yielded 2.60.
  • Simulations indicated that smaller sample sizes (below 125) reduced the precision of LCFA-based MIC estimates and increased non-convergence rates.

Conclusions:

  • LCFA provides an accurate and computationally efficient method for estimating the minimal important change (MIC).
  • The LCFA method is robust, but sample sizes exceeding 125 are preferable for enhanced precision and model stability.
  • This study validates LCFA as a valuable tool for MIC estimation in patient-reported outcome research.