Time Series Disturbance Detection for Hypothesis-Free Signal Detection in Longitudinal Observational Databases

Drug Safety : an International Journal of Medical Toxicology and Drug Experience
Ed WhalenAndrew Bate

Abstract

Signal detection remains a cornerstone activity of pharmacovigilance. Routine quantitative signal detection primarily focuses on screening of spontaneous reports. In striving to enhance quantitative signal detection capability further, other data streams are being considered for their potential contribution as sources of emerging signals, one of which is longitudinal observational databases, including electronic medical record (EMR) and transactional insurance claims databases. Quantitative signal detection on such databases is a nascent field-with published methods being primarily based either on individual metrics, which may not effectively represent the complexity of the longitudinal records and their necessary variation for analysis for drug-outcome pairs, or on visualization discovery approaches leveraging multiple aspects of the records, which are not particularly tractable to high-throughput hypothesis-free signal detection. One extensively tested example of the latter is chronographs. We apply a disturbance detection algorithm to chronographs using UK EMR The Health Improvement Network (THIN) data. The algorithm utilizes autoregressive integrated moving average (ARIMA)-based time series methodology designed to find dist...Continue Reading

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May 12, 2017·Expert Opinion on Drug Safety·Mickael ArnaudFrancesco Salvo

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Citations

Mar 21, 2020·Clinical Pharmacology and Therapeutics·Manfred HaubenPatrick Caubel
Aug 21, 2019·Therapeutic Advances in Drug Safety·Andrew BateRobert F Reynolds
Oct 8, 2020·Drug Safety : an International Journal of Medical Toxicology and Drug Experience·Andrew Bate, Steve F Hobbiger
Feb 3, 2021·Clinical Therapeutics·Paul Beninger

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