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

Author

Listed:
  • Ed Whalen

    (Pfizer Inc)

  • Manfred Hauben

    (Pfizer Inc
    New York University School of Medicine)

  • Andrew Bate

    (Pfizer Inc)

Abstract

Introduction 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. Methods 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 disturbances that occur outside the normal pattern trends of the ARIMA model for the chronograph. Chronographs currently highlight drug–event pairs as potentially worthy of further clinical assessment, via filter-based increases in disproportionality scores from before to after the index drug exposure, tested across a range of case and control windows. Results We replicate the findings on six exemplar chronographs from a previous publication, and show how disturbances can be effectively detected across this set of pairs. Further, 692 disturbances were detected in analysis of all 384 individual READ code outcomes ever recorded 50 or more times for patients prescribed sibutramine. The disturbances are algorithmically further broken into subsets of clinical interest. Conclusion Overall, the disturbance algorithm approach shows promising capacity for detecting outliers, and shows tractability of the algorithmic approach for large-scale screening. The method offers an array of pattern types for detection and clinical review.

Suggested Citation

  • Ed Whalen & Manfred Hauben & Andrew Bate, 2018. "Time Series Disturbance Detection for Hypothesis-Free Signal Detection in Longitudinal Observational Databases," Drug Safety, Springer, vol. 41(6), pages 565-577, June.
  • Handle: RePEc:spr:drugsa:v:41:y:2018:i:6:d:10.1007_s40264-018-0640-8
    DOI: 10.1007/s40264-018-0640-8
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    References listed on IDEAS

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    1. Izyan A. Wahab & Nicole L. Pratt & Lisa Kalisch Ellett & Elizabeth E. Roughead, 2016. "Sequence Symmetry Analysis as a Signal Detection Tool for Potential Heart Failure Adverse Events in an Administrative Claims Database," Drug Safety, Springer, vol. 39(4), pages 347-354, April.
    2. Gebhard Kirchgässner & Jürgen Wolters & Uwe Hassler, 2013. "Introduction to Modern Time Series Analysis," Springer Texts in Business and Economics, Springer, edition 2, number 978-3-642-33436-8, October.
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    Cited by:

    1. Yiqing Zhao & Yue Yu & Hanyin Wang & Yikuan Li & Yu Deng & Guoqian Jiang & Yuan Luo, 2022. "Machine Learning in Causal Inference: Application in Pharmacovigilance," Drug Safety, Springer, vol. 45(5), pages 459-476, May.
    2. Andrew Bate & Steve F. Hobbiger, 2021. "Artificial Intelligence, Real-World Automation and the Safety of Medicines," Drug Safety, Springer, vol. 44(2), pages 125-132, February.

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