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Tree-based latent variable model for assessing differential item functioning in patient-reported outcome measures: a

Olayinka I Arimoro1, Lisa M Lix2, Scott B Patten1

  • 1Department of Community Health Sciences & O'Brien Institute for Public Health, University of Calgary, Calgary, AB, Canada.

Quality of Life Research : an International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation
|July 18, 2025
PubMed
Summary
This summary is machine-generated.

The Partial Credit Model based on Recursive Partitioning (PCTree) effectively controls for differential item functioning (DIF) in patient-reported outcome measures (PROMs). This statistical method demonstrates robust performance, especially with Bonferroni correction, ensuring reliable results in heterogeneous populations.

Keywords:
Differential item functioningLatent variable modelPartial credit modelPatient-reported outcome measuresStatistical powerType I error

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Area of Science:

  • Psychometrics
  • Statistical Modeling
  • Health Outcomes Research

Background:

  • Patient-reported outcome measures (PROMs) are crucial for assessing health status.
  • Heterogeneity in patient responses can lead to differential item functioning (DIF), threatening PROM validity.
  • Existing tree-based latent variable models, like PCTree, require further performance evaluation for DIF detection.

Purpose of the Study:

  • To evaluate the statistical properties of the Partial Credit Model based on Recursive Partitioning (PCTree) for detecting DIF in polytomous PROMs.
  • To assess the performance of PCTree under various data-analytic conditions.

Main Methods:

  • Computer simulations were employed to assess PCTree's performance with and without Bonferroni adjustments.
  • Type I error and statistical power rates were used to evaluate model performance.
  • Robustness was assessed using Bradley's liberal criterion for familywise Type I error rate control.

Main Results:

  • PCTree with Bonferroni correction demonstrated effective control of the familywise Type I error rate across all simulated conditions.
  • Statistical power reached at least 80% for sample sizes (N) of 500 or greater.
  • Power decreased as the number of non-DIF-associated explanatory variables increased.

Conclusions:

  • PCTree shows promise as a method for evaluating DIF in diverse patient populations.
  • Recommendations for optimal data analytic conditions for utilizing PCTree are provided.