Abstract
Background
Children are frequently prescribed medication ‘off-label’, meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. The main reason this safety knowledge is lacking is due to ethical restrictions that prevent children from being included in the majority of clinical trials.
Objective
The objective of this paper is to investigate whether an ensemble of simple study designs can be implemented to signal acutely occurring side effects effectively within the paediatric population by using historical longitudinal data. The majority of pharmacovigilance techniques are unsupervised, but this research presents a supervised framework.
Methods
Multiple measures of association are calculated for each drug and medical event pair and these are used as features that are fed into a classifier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classifier is trained using known adverse drug reactions or known non-adverse drug reaction relationships.
Results
The novel ensemble framework obtained a false positive rate of 0.149, a sensitivity of 0.547 and a specificity of 0.851 when implemented on a reference set of drug and medical event pairs. The novel framework consistently outperformed each individual simple study design.
Conclusion
This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
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Acknowledgments
This study was funded by the Department of Computer Sciences, University of Nottingham, UK. J.M. Reps, J.M. Garibaldi, U. Aickelin, D. Soria, J.E. Gibson and R.B. Hubbard have no conflicts of interest that are directly relevant to the content of this study.
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Reps, J.M., Garibaldi, J.M., Aickelin, U. et al. Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs. Drug Saf 37, 163–170 (2014). https://doi.org/10.1007/s40264-014-0137-z
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DOI: https://doi.org/10.1007/s40264-014-0137-z