Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining
This work addresses the challenge of improving signal accuracy in pharmacovigilance for healthcare professionals, though it is incremental as it builds on existing methods for confounding adjustment.
The researchers tackled the problem of refining adverse drug reaction signals by developing a framework that identifies confounding interaction terms using emergent pattern mining and incorporates them into regularized Cox regression, resulting in correct ranking of true adverse drug reactions above false ones in a cohort study.
Purpose: To develop a framework for identifying and incorporating candidate confounding interaction terms into a regularised cox regression analysis to refine adverse drug reaction signals obtained via longitudinal observational data. Methods: We considered six drug families that are commonly associated with myocardial infarction in observational healthcare data, but where the causal relationship ground truth is known (adverse drug reaction or not). We applied emergent pattern mining to find itemsets of drugs and medical events that are associated with the development of myocardial infarction. These are the candidate confounding interaction terms. We then implemented a cohort study design using regularised cox regression that incorporated and accounted for the candidate confounding interaction terms. Results The methodology was able to account for signals generated due to confounding and a cox regression with elastic net regularisation correctly ranked the drug families known to be true adverse drug reactions above those.