Comparison of structural equation modelling, item response theory and Rasch measurement theory-based methods for response shift detection at item level: A simulation study

When assessing change in patient-reported outcomes, the meaning in patients' self-evaluations of the target construct is likely to change over time. Therefore, methods evaluating longitudinal measurement non-invariance or response shift at item-level were proposed, based on structural equation modelling or on item response theory. Methods coming from Rasch measurement theory could also be valuable. The lack of evaluation of these approaches prevents determining the best strategy to adopt. A simulation study was performed to compare and evaluate the performance of structural equation modelling, item response theory and Rasch measurement theory approaches for item-level response shift detection. Performances of these three methods in different situations were evaluated with the rate of false detection of response shift (when response shift was not simulated) and the rate of correct response shift detection (when response shift was simulated). The Rasch measurement theory-based method performs better than the structural equation modelling and item response theory-based methods when recalibration was simulated. Consequently, the Rasch measurement theory-based approach should be preferred for studies investigating only recalibration response shift at item-level. For structural equation modelling and item response theory, the low rates of reprioritization detection raise issues on the potential different meaning and interpretation of reprioritization at item-level.