Propensity score-adjusted three-component mixture model for drug-drug interaction data mining in FDA Adverse Event Reporting System.

ABSTRACT: With increasing trend of polypharmacy, drug-drug interaction (DDI)-induced adverse drug events (ADEs) are considered as a major challenge for clinical practice. As premarketing clinical trials usually have stringent inclusion/exclusion criteria, limited comedication data capture and often times small sample size have limited values in study DDIs. On the other hand, ADE reports collected by spontaneous reporting system (SRS) become an important source for DDI studies. There are two major challenges in detecting DDI signals from SRS: confounding bias and false positive rate. In this article, we propose a novel approach, propensity score-adjusted three-component mixture model (PS-3CMM). This model can simultaneously adjust for confounding bias and estimate false discovery rate for all drug-dr

SUBMITTER: Wang X 

PROVIDER: S-EPMC9292662 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature