Abstract
Polypharmacy in older adults increases the risk of adverse drug reactions (ADRs), but studying this relationship is complex. In real-world data, the high number of medications, coupled with rare drug combinations, results in high-dimensional datasets that are difficult to analyze using conventional statistical methods. This study applies horseshoe and lasso regression for analyzing rare events in polypharmacy contexts, focusing on severe ADRs such as falls and bleedings. These regression models are executed on a multi-center dataset compiling 7175 cases from the ADRED project to detect potential ADR-associated drugs among 100 most common drugs in emergency department admissions. Positive predictors are classified by using 50% and 90% credibility intervals. This study demonstrates that regression models with horseshoe or lasso priors are effective for analyzing ADRs, providing a comprehensive consideration of multiple factors in large, sparse datasets and improving signal detection in polypharmacy, addressing a significant challenge in pharmacovigilance. Both priors yielded consistent and clinically meaningful results. The horseshoe regression resulted in fewer potential positive predictors overall, which could make it suitable as a diagnostic tool. While these regressions generate valuable information, there are still challenges in setting appropriate thresholds for determining and interpreting the positive results.