The use of patient-reported outcomes measures is gaining popularity in clinical trials for comparing patient groups. Such comparisons typically focus on the differences in group means and are carried out using either a traditional sum-score-based approach or item response theory (IRT)-based approaches. Several simulation studies have evaluated different group mean comparison approaches in the past, but the performance of these approaches remained unknown under certain uninvestigated conditions (e.g. under the impact of differential item functioning (DIF)). By incorporating some of the uninvestigated simulation features, the current study examines Type I error, statistical power, and effect size estimation accuracy associated with group mean comparisons using simple sum scores, IRT model likelihood ratio tests, and IRT expected-a-posteriori scores. Manipulated features include sample size per group, number of items, number of response categories, strength of discrimination parameters, location of thresholds, impact of DIF, and presence of missing data. Results are summarized and visualized using decision trees
