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Item response theory (IRT) scores offer more accurate analysis of randomized controlled trials (RCTs) than traditional sum scores. Using IRT improves precision in estimating patient outcomes and group effects, avoiding significant bias found with sum scores.

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Area of Science:

  • Psychometrics
  • Clinical Trials
  • Health Outcomes Research

Background:

  • Patient-reported outcomes (PROs) are crucial in randomized controlled trials (RCTs), often measured using questionnaires.
  • Classical test theory (CTT) sum scores are commonly used, but they can overlook nuances in response patterns.
  • Item response theory (IRT) offers a more sophisticated measurement model for PROs.

Purpose of the Study:

  • To demonstrate the advantages of employing IRT scores over CTT sum scores in RCT data analysis.
  • To highlight how IRT enhances the precision of estimating individual and group effects over time.

Main Methods:

  • A comparative analysis using both IRT and sum scores was conducted.
  • Two distinct studies were utilized: a real-world RCT and a simulation study.
  • Both measurement models were applied to assess the same construct and calculate outcomes for effect estimation.

Main Results:

  • Sum scores introduced a significant bias (approximately one standard deviation) in estimated trends within RCT results.
  • IRT-based scores demonstrated negligible bias, leading to more accurate trend estimations.
  • The choice of measurement model directly impacts the reliability and accuracy of RCT findings.

Conclusions:

  • Accurate statistical inferences in RCTs necessitate the use of IRT for construct measurement.
  • Employing CTT sum scores can lead to erroneous conclusions and biased results in RCTs.
  • IRT provides a superior framework for analyzing PRO data in clinical trials, enhancing scientific validity.