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Related Experiment Video

Updated: Jan 16, 2026

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Do complex psychometric analyses really matter? Comparing multiple approaches using individual participant data from

David Byrne1, Frank Doyle2, Susan Brannick3

  • 1School of Population Health, Dublin, Ireland.

Psychological Medicine
|October 1, 2025
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Summary

Different psychometric methods impact clinical trial outcomes differently. Factor analysis (FA) increased effect sizes, while item response theory (IRT) and network analysis (NA) showed negligible changes for antidepressant trials.

Keywords:
antidepressant agentdepressive disorderpsychiatric status rating scalespsychometricsrandomized controlled trails

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Area of Science:

  • Psychometrics
  • Clinical Trials
  • Mental Health Research

Background:

  • Psychometric methods refine measures by removing underperforming items and reducing error.
  • Varying psychometric approaches can yield divergent results in data analysis.
  • This study investigates the impact of different psychometric methods on clinical trial outcomes.

Purpose of the Study:

  • To compare the implications of applying factor analysis (FA), item response theory (IRT), and network analysis (NA) to clinical trial data.
  • To assess how different psychometric modeling approaches affect effect size calculations in antidepressant treatment trials.
  • To determine the most effective psychometric method for identifying differential outcomes between placebo and treatment groups.

Main Methods:

  • Utilized individual participant data from 15 antidepressant treatment trials sourced from Vivli.org.
  • Applied factor analysis (FA), item response theory (IRT), and network analysis (NA) to Montgomery-Asberg Depression Rating Scale outcome data (baseline and 8-week).
  • Assessed trial outcomes by comparing Cohen's *d* effect sizes between original summative scores and psychometrically modeled scores using multilevel models.

Main Results:

  • All methods produced unidimensional models, with scale lengths ranging from 7 to 10 items.
  • Item response theory (IRT) showed unchanged treatment effects (d=0.072).
  • Network analysis (NA) decreased effect sizes by 1.3%-2.8% (d=0.070-0.071).
  • Factor analysis (FA) increased effect sizes by 11%-12.5% (d=0.080-0.081).

Conclusions:

  • Item response theory (IRT) and network analysis (NA) resulted in negligible differences in effect outcomes compared to original trial data.
  • Factor analysis (FA) demonstrated an increase in effect sizes, suggesting its potential effectiveness in highlighting differences between placebo and treatment groups.
  • FA may be the optimal method for identifying specific items that differentiate treatment and placebo responses in depression studies.