Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining

TL;DR

This paper presents a framework that enhances adverse drug reaction signal detection by identifying and integrating interaction variables through emergent pattern mining and regularised Cox regression, improving causal inference accuracy.

Contribution

It introduces a novel approach combining emergent pattern mining with regularised Cox regression to refine adverse drug reaction signals in observational data.

Findings

Successfully identified confounding interaction terms.

Improved ranking of true adverse drug reactions.

Effectively distinguished causal signals from confounding.

Abstract

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.