Performance of a Rasch-based method for group comparisons of longitudinal change and response shift at the item level...

The growing interest in patient perception and experience in healthcare has led to an increase in the use of patient-reported outcomes (PRO) data. However, chronically ill patients may regularly adapt to their disease and, as a consequence, might change their perception of the PRO being measured. This phenomenon named response shift (RS) may occur differently depending on clinical and individual characteristics. The RespOnse Shift ALgorithm at the Item level (ROSALI), a method for RS analysis at the item level based on Rasch models, has recently been extended to explore heterogeneity of item-level RS between two groups of patients. The performances of ROSALI in terms of RS detection at the item level and biases of estimated differences in latent variable means were assessed. A simulation study was performed to investigate four scenarios: no RS, RS in only one group, RS affecting both groups either in a similar or a different way. Performances of ROSALI were assessed using rates of false detection of RS when no RS was simulated and a set of criteria (presence of RS, correct identification of items and groups affected by RS) when RS was simulated. Rates of false detection of RS were low indicating that ROSALI satisfactorily prevents from mistakenly inferring RS. ROSALI is able to detect RS and identify the item and group(s) affected when RS affects all response categories of an item in the same way. The performances of ROSALI depend mainly on the sample size and the degree of heterogeneity of item-level RS.