TEDAR: Temporal dynamic signal detection of adverse reactions

Computational approaches to detect the signals of adverse drug reactions are powerful tools to monitor the unattended effects that users experience and report, also preventing death and serious injury. They apply statistical indices to affirm the validity of adverse reactions reported by users. The methodologies that scan fixed duration intervals in the lifetime of drugs are among the most used. Here we present a method, called TEDAR, in which ranges of varying length are taken into account. TEDAR has the advantage to detect a greater number of true signals without significantly increasing the number of false positives, which are a major concern for this type of tools. Furthermore, early detection of signals is a key feature of methods to prevent the safety of the population. The results show that TEDAR detects adverse reactions many months earlier than methodologies based on a fixed interval length.

[1]  Postmarketing Spontaneous Pharmacovigilance Reporting Systems , 2019, Pharmacoepidemiology.

[2]  Inkyung Jung,et al.  Comparison of Data Mining Methods for the Signal Detection of Adverse Drug Events with a Hierarchical Structure in Postmarketing Surveillance , 2020, Life.

[3]  M. Sardella,et al.  Evaluation of quantitative signal detection in EudraVigilance for orphan drugs: possible risk of false negatives , 2019, Therapeutic advances in drug safety.

[4]  Robert T. Chen,et al.  Morbidity and Mortality Weekly Report , 2002 .

[5]  Yanqing Ji,et al.  A Multi-relational Association Mining Algorithm for Screening Suspected Adverse Drug Reactions , 2014, 2014 11th International Conference on Information Technology: New Generations.

[6]  Sushil Ghimire,et al.  Burden of hospitalizations related to adverse drug events in the USA: a retrospective analysis from large inpatient database , 2017, Pharmacoepidemiology and drug safety.

[7]  Barbara Osimani,et al.  New Insights in Computational Methods for Pharmacovigilance: E-Synthesis, a Bayesian Framework for Causal Assessment , 2019, International journal of environmental research and public health.

[8]  Jing Huang,et al.  A signal detection method for temporal variation of adverse effect with vaccine adverse event reporting system data , 2017, BMC Medical Informatics and Decision Making.

[9]  M. Lerch,et al.  Statistical Signal Detection as a Routine Pharmacovigilance Practice: Effects of Periodicity and Resignalling Criteria on Quality and Workload , 2015, Drug Safety.

[10]  A. Bate,et al.  Quantitative signal detection using spontaneous ADR reporting , 2009, Pharmacoepidemiology and drug safety.

[11]  Ismaïl Ahmed,et al.  Class-imbalanced subsampling lasso algorithm for discovering adverse drug reactions , 2018, Statistical methods in medical research.

[12]  Marianthi Markatou,et al.  An evaluation of statistical approaches to postmarketing surveillance , 2020, Statistics in medicine.

[13]  A. Bate,et al.  A Bayesian neural network method for adverse drug reaction signal generation , 1998, European Journal of Clinical Pharmacology.

[14]  Kristina Juhlin,et al.  Comparison of Statistical Signal Detection Methods Within and Across Spontaneous Reporting Databases , 2015, Drug Safety.

[15]  Kenneth J Rothman,et al.  The reporting odds ratio and its advantages over the proportional reporting ratio , 2004, Pharmacoepidemiology and drug safety.

[16]  Quan Xu,et al.  ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms , 2014, Nucleic Acids Res..

[17]  M. Narukawa,et al.  Significance of data mining in routine signal detection: Analysis based on the safety signals identified by the FDA , 2018, Pharmacoepidemiology and drug safety.

[18]  William DuMouchel,et al.  Bayesian Data Mining in Large Frequency Tables, with an Application to the FDA Spontaneous Reporting System , 1999 .

[19]  S. Evans,et al.  Use of proportional reporting ratios (PRRs) for signal generation from spontaneous adverse drug reaction reports , 2001, Pharmacoepidemiology and drug safety.

[20]  Peer Bork,et al.  The SIDER database of drugs and side effects , 2015, Nucleic Acids Res..

[21]  G. Niklas Norén,et al.  Good Signal Detection Practices: Evidence from IMI PROTECT , 2016, Drug Safety.

[22]  Jim Slattery,et al.  Validation of Statistical Signal Detection Procedures in EudraVigilance Post-Authorization Data , 2010, Drug safety.