In order to investigate patients’ experience of healthcare, repeated assessments of patient-reported outcomes (PRO) are increasingly performed in observational studies and clinical trials. Changes in PRO can however be difficult to interpret in longitudinal settings as patients’ perception of the concept being measured may change over time, leading to response shift (longitudinal measurement non-invariance) and possibly to erroneous interpretation of the observed changes in PRO. Several statistical methods for response shift analysis have been proposed, but they usually assume that response shift occurs in the same way in all individuals within the sample regardless of their characteristics. Many studies aim at comparing the longitudinal change of PRO into two groups of patients (treatment arm, different pathologies, …). The group variable could have an effect on PRO change but also on response shift effect and the perception of the questionnaire at baseline. In this paper, we propose to enhance the ROSALI algorithm based on Rasch Measurement Theory for the analysis of longitudinal PRO data to simultaneously investigate the effects of group on item functioning at the first measurement occasion, on response shift and on changes in PRO over time. ROSALI is subsequently applied to a longitudinal dataset on change in emotional functioning in patients with breast cancer or melanoma during the year following diagnosis. The use of ROSALI provides new insights in the analysis of longitudinal PRO data.

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 Item level (ROSALI), a method for RS analysis at item-level based on Rasch models, has recently been extended to explore heterogeneity of itemlevel RS between two groups of patients. The performances of ROSALI in terms of RS detection at item-level and bias 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 concluding erroneously to RS. ROSALI is able to correctly conclude to the presence of RS and identify the item and the group(s) affected by RS when RS affected 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.