Abstract

Objectives

This study aimed to examine the ability of classical test theory (CTT) and item response theory (IRT) scores assessed by Patient-Reported Outcomes Measurement Information System® (PROMIS®) measures to identify significant individual changes in the setting of clinical studies, using both simulated and empirical data.

Methods

We used simulated data to compare the estimation of significant individual changes between CTT and IRT scores across different conditions and a clinical trial data set to verify the simulation results. We calculated reliable change indexes to estimate significant individual changes.

Results

For small true change, IRT scores showed a slightly higher rate of classifying change groups than CTT scores and were comparable with CTT scores for a shorter test length. Additionally, IRT scores were found to have a prominent advantage in the classification rates of change groups for medium to high true change over CTT scores. Such an advantage became prominent in a longer test length. The empirical data analysis results using an anchor-based approach further supported the above findings that IRT scores can more accurately classify participants into change groups than CTT scores.

Conclusions

Given that IRT scores perform better, or at least comparably, in most conditions, we recommend using IRT scores to estimate significant individual changes and identify responders to treatment. This study provides evidence-based guidance in detecting individual changes based on CTT and IRT scores under various measurement conditions and leads to recommendations for identifying responders to treatment for participants in clinical trials.