Triple Challenges – Small Sample Sizes in Both Exposure and Control Groups When Scanning Rare Maternal Outcomes in Signal Identification: A Simulation Study

TreeScan™ is a signal identification approach that scans thousands of outcomes simultaneously while adjusting for multiple hypothesis testing to identify potential safety signals. The Poisson model used in TreeScan assumes that expected incidence proportions are known without error.

The presentation evaluates how expected incidence proportions estimated from small control groups may affect TreeScan’s ability to identify signals for maternal adverse outcomes. It was presented at the 39th International Conference on Pharmacoepidemiology and Therapeutic Risk Management.