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

Reps, Jenna M.; Aickelin, Uwe; Hubbard, Richard B.

Authors

Jenna M. Reps

Uwe Aickelin

RICHARD HUBBARD richard.hubbard@nottingham.ac.uk
Blf/Gsk Professor of Epidemiological Resp Research



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 ranking the drug families known to be true adverse drug reactions above those that are not. This was not the case without the inclusion of the candidate confounding interaction terms, where confounding leads to a non-adverse drug reaction being ranked highest.
Conclusions: The methodology is efficient, can identify high-order confounding interactions and does not require expert input to specify outcome specific confounders, so it can be applied for any outcome of interest to quickly refine its signals. The proposed method shows excellent potential to overcome some forms of confounding and therefore reduce the false positive rate for signal analysis using longitudinal data.

Citation

Reps, J. M., Aickelin, U., & Hubbard, R. B. (2016). Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining. Computers in Biology and Medicine, 69, https://doi.org/10.1016/j.compbiomed.2015.11.014

Journal Article Type Article
Acceptance Date Nov 24, 2015
Online Publication Date Dec 4, 2015
Publication Date Feb 1, 2016
Deposit Date Jun 15, 2016
Publicly Available Date Jun 15, 2016
Journal Computers in Biology and Medicine
Print ISSN 0010-4825
Electronic ISSN 0010-4825
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 69
DOI https://doi.org/10.1016/j.compbiomed.2015.11.014
Public URL http://eprints.nottingham.ac.uk/id/eprint/34048
Publisher URL http://www.sciencedirect.com/science/article/pii/S0010482515003820
Copyright Statement Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0





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