A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations - Reps, Jenna M.; Garibaldi, Jonathan M.; Aickelin, Uwe; Gibson, Jack E.; Hubbard, Richard B. (Elsevier, 2015)
Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data.