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 assoc...
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| Format: | Article |
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Elsevier
2016
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| Online Access: | https://eprints.nottingham.ac.uk/34048/ |
| _version_ | 1848794762388176896 |
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| author | Reps, Jenna M. Aickelin, Uwe Hubbard, Richard B. |
| author_facet | Reps, Jenna M. Aickelin, Uwe Hubbard, Richard B. |
| author_sort | Reps, Jenna M. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T19:21:21Z |
| format | Article |
| id | nottingham-34048 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:21:21Z |
| publishDate | 2016 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-340482020-05-04T17:29:05Z https://eprints.nottingham.ac.uk/34048/ Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining Reps, Jenna M. Aickelin, Uwe Hubbard, Richard B. 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. Elsevier 2016-02-01 Article PeerReviewed Reps, Jenna M., Aickelin, Uwe and Hubbard, Richard B. (2016) Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining. Computers in Biology and Medicine, 69 . pp. 61-70. ISSN 0010-4825 http://www.sciencedirect.com/science/article/pii/S0010482515003820 doi:10.1016/j.compbiomed.2015.11.014 doi:10.1016/j.compbiomed.2015.11.014 |
| spellingShingle | Reps, Jenna M. Aickelin, Uwe Hubbard, Richard B. Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining |
| title | Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining |
| title_full | Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining |
| title_fullStr | Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining |
| title_full_unstemmed | Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining |
| title_short | Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining |
| title_sort | refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining |
| url | https://eprints.nottingham.ac.uk/34048/ https://eprints.nottingham.ac.uk/34048/ https://eprints.nottingham.ac.uk/34048/ |