title: Signal detection using change point analysis in postmarket surveillance.
abstract: OBJECTIVE
  Signal detection methods have been used extensively in postmarket surveillance to identify elevated risks of adverse events associated with medical products (drugs, vaccines, and devices). However, current popular disproportionality methods ignore useful information such as trends when the data are aggregated over time for signal detection.
  
  
  METHODS
  In this paper, we applied change point analysis (CPA) to trend analysis of medical products in a spontaneous adverse event reporting system. CPA was used to detect the time point at which statistical properties of a sequence of observations change over time. Two CPA approaches, change in mean and change in variance, were demonstrated by an example using neurostimulator adverse event dataset.
  
  
  RESULTS
  Two significant change points associated with upward trends were detected in June 2008 (n = 20, p < 0.001) and May 2011 (n = 51, p = 0.003). Further investigation confirmed battery issues and expansion of the indication for use could be possible causes for the occurrence of these change points. Two time points showed extremely low number of loss of therapy events, two cases in October 2009 and three in November 2009, which could be the result of reporting issues such as underreporting.
  
  
  CONCLUSIONS
  As a complimentary tool to current signal detection efforts at FDA, CPA can be used to detect changes in the association between medical products and adverse events over time. Detecting these changes could be critical for public health regulation, adverse events surveillance, product recalls, and regulators' understanding of the connection between adverse events and other events regarding regulated products. © 2015 The Authors. Pharmacoepidemiology and Drug Safety published by John Wiley & Sons, Ltd.
authors.affiliation: Division of Biostatistics, U.S. Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Zhiheng
authors.initials: Z
authors.lastname: Xu
authors.affiliation: Chief Health Informatics Officer, Chief Technology Officer, Office of Informatics and Technology Innovation, Office of Operations, Office of the Commissioner, U.S. Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Taha
authors.initials: T
authors.lastname: Kass-Hout
authors.affiliation: Division of Epidemiology, U.S. Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Colin
authors.initials: C
authors.lastname: Anderson-Smits
authors.affiliation: Division of Biostatistics, U.S. Food and Drug Administration, Silver Spring, MD, USA.
authors.forename: Gerry
authors.initials: G
authors.lastname: Gray
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D016208:Databases, Factual; D006801:Humans; D017891:Pharmacoepidemiology / Q000379:methods*; D011159:Population Surveillance / Q000379:methods; D011237:Predictive Value of Tests; D014481:United States / Q000453:epidemiology; D014486:United States Food and Drug Administration
keywords: adverse event; change point analysis; pharmacoepidemiology; signal detection