Supervised Multidimensional Item Response Theory Modeling of Pediatric Iatrogenic Withdrawal Symptoms

Item-level data from composite scales can be analyzed with pharmacometric item response theory (IRT) models to improve the quantification of disease severity compared with the use of total composite scores. However, regular IRT models assume unidimensionality, which is violated in the scale measuring iatrogenic withdrawal in children because some items are also affected by pain, undersedation, or delirium. Here, we compare regular IRT modelling of pediatric iatrogenic withdrawal symptom data with two new analysis approaches in which the latent variable is guided towards the condition of interest using numerical withdrawal severity scored by nurses as a "supervising variable:" supervised IRT (sIRT) and supervised multi-dimensional (smIRT) modelling. In this example, in which the items scores are affected by multiple conditions, regular IRT modeling is worse to quantify disease severity than the total composite score, whereas improved performance compared with the composite score is observed for the sIRT and smIRT models.