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Estimating meaningful thresholds for multi-item questionnaires using item response theory.

Berend Terluin^1,2, Jaimy E Koopman³, Lisa Hoogendam^3,4

¹Department of General Practice, Amsterdam UMC Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands. b.terluin@amsterdamumc.nl.

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Summary

A new item response theory (IRT) method accurately estimates meaningful thresholds for patient-reported outcome measures (PROMs). This approach provides reliable interpretation of PROM results, outperforming other methods in various simulated and real-world datasets.

Keywords:

Adjusted predictive modeling Cutoff point Item response theory (IRT)Meaningful threshold Patient acceptable symptom state (PASS)Receiver operating characteristic (ROC)

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Area of Science:

Psychometrics
Health Outcomes Research

Background:

Interpreting patient-reported outcome measures (PROMs) requires meaningful thresholds.
Current methods for threshold estimation may yield biased results.

Purpose of the Study:

Introduce and evaluate a novel item response theory (IRT) based method for estimating PROM thresholds.
Compare the performance of the new IRT method against existing threshold estimation techniques.

Main Methods:

Fit an IRT model to PROM items and an anchor item representing the criterion state.
Utilize the anchor item's difficulty parameter as the threshold on the latent trait.
Simulated 4500 datasets and analyzed two real-world datasets (depression and pain scales).

Main Results:

The new IRT method accurately recovered true thresholds in simulations.
Alternative methods showed bias, especially when state prevalence deviated from 0.5.
Adjusted predictive modeling showed similar performance but with residual bias at extreme prevalences.

Conclusions:

The proposed IRT method reliably estimates meaningful thresholds for multi-item questionnaires.
This method is accurate when data meet IRT assumptions, enhancing PROM interpretation.