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A statistical signalling model for use in surveillance of adverse drug reaction data

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  • Eric Hillson
  • Jaxk Reeves
  • Charlotte Mcmillan

Abstract

This paper presents a statistically superior lag-adjusted model for detecting increased frequency of reports of adverse drug event (ADE) rates. The effect of a significant lag time between ADE occurrence and report dates is studied. The approach in this paper to analyzing ADE data of this nature involves proposing a statistical model that utilizes a lag density function. The statistical method proposed was the development of an 'exact' procedure to monitor drugs that have a low incidence of ADEs. The approach determines statistically whether a change in the frequency of a specific ADE exists between two predetermined time intervals. There exist immense public health implications associated with the early detection of serious ADEs. The reduced risk of unfavorable outcomes associated with medication therapy is the goal of all involved. Simulated illustrations and discussion are provided, along with a detailed FORTRAN program used to implement the newly suggested lag-adjusted procedure.

Suggested Citation

  • Eric Hillson & Jaxk Reeves & Charlotte Mcmillan, 1998. "A statistical signalling model for use in surveillance of adverse drug reaction data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 25(1), pages 23-40.
    • Handle: RePEc:taf:japsta:v:25:y:1998:i:1:p:23-40
    DOI: 10.1080/02664769823287
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    Cited by:

    1. Christian Sonesson, 2003. "Evaluations of some Exponentially Weighted Moving Average methods," Journal of Applied Statistics, Taylor & Francis Journals, vol. 30(10), pages 1115-1133.
    2. Christian Sonesson & David Bock, 2003. "A review and discussion of prospective statistical surveillance in public health," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 166(1), pages 5-21, February.

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