IDEAS home Printed from https://ideas.repec.org/a/spr/drugsa/v41y2018i6d10.1007_s40264-018-0640-8.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40264-018-0640-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s40264-018-0640-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Aurelie Charles & Damiano Sguotti, 2021. "Sustainable Earnings: How Can Herd Behavior in Financial Accumulation Feed into a Resilient Economic System?," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    2. Bandyopadhyay, Kaushik Ranjan, 2009. "Does OPEC act as a Residual Producer?," MPRA Paper 25841, University Library of Munich, Germany, revised 2010.
    3. Nsisong Patrick Ekong & Daniel Wilson Ebong, 2016. "On the Crude Oil Price, Stock Market Movement and Economic Growth Nexus in Nigeria Evidence from Cointegration and Var Analysis," Asian Journal of Economic Modelling, Asian Economic and Social Society, vol. 4(3), pages 112-123, September.
    4. Erik Heilmann & Janosch Henze & Heike Wetzel, 2021. "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics 202135, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    5. Orkun ÇELİK & Deniz ERER & Elif ERER, 2018. "2008 Küresel Krizinin Bireysel Emeklilik Fonları Oynaklığı Üzerindeki Etkisi: Türkiye Örneği," Sosyoekonomi Journal, Sosyoekonomi Society, issue 26(35).
    6. Till Strohsal & Christian R. Proaño & Jürgen Wolters, 2019. "Assessing the cross-country interaction of financial cycles: evidence from a multivariate spectral analysis of the USA and the UK," Empirical Economics, Springer, vol. 57(2), pages 385-398, August.
    7. Tüzemen Samet & Barış-Tüzemen Özge & Çelik Ali Kemal, 2021. "The relationship between information and communication technologies and female labour force participation in Turkey," Economics and Business Review, Sciendo, vol. 7(4), pages 121-145, December.
    8. Lena Dräger, 2015. "Inflation perceptions and expectations in Sweden – Are media reports the missing link?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 77(5), pages 681-700, October.
    9. Maparu, Tuhin Subhra & Mazumder, Tarak Nath, 2017. "Transport infrastructure, economic development and urbanization in India (1990–2011): Is there any causal relationship?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 319-336.
    10. Lukasz Marc, 2014. "The Causal Links between Aid and Government Expenditures," Tinbergen Institute Discussion Papers 14-012/V, Tinbergen Institute.
    11. Lars P. Feld, 2017. "In memoriam: Gebhard Kirchgässner (April 15, 1948–April 1, 2017)," Public Choice, Springer, vol. 172(3), pages 305-310, September.
    12. Belke, Ansgar & Gokus, Christian, 2011. "Volatility Patterns of CDS, Bond and Stock Markets Before and During the Financial Crisis – Evidence from Major Financial Institutions," Ruhr Economic Papers 243, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    13. Christian Dreger & Jürgen Wolters, 2010. "M3 money demand and excess liquidity in the euro area," Public Choice, Springer, vol. 144(3), pages 459-472, September.
    14. Gebhard Kirchgässner & Jürgen Wolters, 2010. "The Role of Monetary Aggregates in the Policy Analysis of the Swiss National Bank," Swiss Journal of Economics and Statistics (SJES), Swiss Society of Economics and Statistics (SSES), vol. 146(I), pages 221-253, March.
    15. Mustofa Usman & Luvita Loves & Edwin Russel & Muslim Ansori & Warsono Warsono & Widiarti Widiarti & Wamiliana Wamiliana, 2022. "Analysis of Some Energy and Economics Variables by Using VECMX Model in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 91-102, March.
    16. Hassler Uwe & Wolters Jürgen, 2009. "Hysteresis in Unemployment Rates? A Comparison between Germany and the US," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 229(2-3), pages 119-129, April.
    17. Hernandez-Villafuerte, Karla Vanessa, 2010. "The relationship between spatial price transmission and geographical distance: the case of Brazil," 116th Seminar, October 27-30, 2010, Parma, Italy 95030, European Association of Agricultural Economists.
    18. Jan Jakub Szczygielski & Chimwemwe Chipeta, 2023. "Properties of returns and variance and the implications for time series modelling: Evidence from South Africa," Modern Finance, Modern Finance Institute, vol. 1(1), pages 35-55.
    19. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
    20. Mustofa Usman & M. Komarudin & Munti Sarida & Wamiliana Wamiliana & Edwin Russel & Mahatma Kufepaksi & Iskandar Ali Alam & Faiz A.M. Elfaki, 2022. "Analysis of Some Variable Energy Companies by Using VAR(p)-GARCH(r,s) Model : Study From Energy Companies of Qatar over the Years 2015 2022," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 178-191, September.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:drugsa:v:41:y:2018:i:6:d:10.1007_s40264-018-0640-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com/economics/journal/40264 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.