Adverse Drug Effects (ADEs) pose significant risks, especially in vulnerable populations such as children, who often lack representation in clinical trials. In this work, we explored the identification of statistically significant associations between drug administration and ADEs (pharmacovigilance signals) in pediatric patients using spontaneous drug safety databases. By applying the Regression adjusted gamma-Poisson shrinkage (RGPS) model, which produces age-adjusted disproportionality measures, the study aims to improve the precision and accuracy of signal detection compared to other models. The results suggest that the RGPS model selects signals with greater precision, particularly when considering age effects. This emphasizes the importance of age-adjusted models in pharmacovigilance efforts to enhance drug safety monitoring and regulatory decision making for pediatric populations.