A Precision Mixture Risk Model to Identify Adverse Drug Events in Subpopulations Using a Case-Crossover Design.

ABSTRACT: Despite the success of pharmacovigilance studies in detecting signals of adverse drug events (ADEs) from real-world data, the risks of ADEs in subpopulations warrant increased scrutiny to prevent them in vulnerable individuals. Recently, the case-crossover design has been implemented to leverage large-scale administrative claims data for ADE detection, while controlling both observed confounding effects and short-term fixed unobserved confounding effects. Additionally, as the case-crossover design only includes cases, subpopulations can be conveniently derived. In this manuscript, we propose a precision mixture risk model (PMRM) to identify ADE signals from subpopulations under the case-crossover design. The proposed model is able to identify signals from all ADE-subpopulation-drug combina

SUBMITTER: Shi Y 

PROVIDER: S-EPMC11994119 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature