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