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Item Response Theory (IRT) models have received growing interest in healthscience for analyzing latent constructs such as depression, anxiety, quality oflife, or cognitive functioning from the information provided by eachindividual's items responses. However, in the presence of repeated itemmeasures, IRT methods usually assume that the measurement occasions are made atthe exact same time for all patients. In this paper, we show how the IRTmethodology can be combined with the mixed model theory to provide alongitudinal IRT model which exploits the information of a measurement scaleprovided at the item level while simultaneously handling observation times thatmay vary across individuals and items. The latent construct is a latent processdefined in continuous time that is linked to the observed item responsesthrough a measurement model at each individual- and occasion-specificobservation time; we focus here on a Graded Response Model for binary andordinal items. The Maximum Likelihood Estimation procedure of the model isavailable in the R package lcmm. The proposed approach is contextualized in aclinical example in end-stage renal disease, the PREDIALA study. The objectiveis to study the trajectories of depressive symptomatology (as measured by 7items of the Hospital Anxiety and Depression scale) according to the time fromregistration on the renal transplant waiting list and the renal replacementtherapy. We also illustrate how the method can be used to assess DifferentialItem Functioning and lack of measurement invariance over time.

Modeling repeated self-reported outcome data: a continuous-time longitudinal Item Response Theory model