title: Postmarketing surveillance of potentially fatal reactions to oncology drugs: potential utility of two signal-detection algorithms.
abstract: OBJECTIVE
  Several data mining algorithms (DMAs) are being studied in hopes of enhancing screening of large post-marketing safety databases for signals of novel adverse events (AEs). The objective of this study was to apply two DMAs to the United States FDA Adverse Event Reporting System (AERS) database to see whether signals of potentially fatal AEs with cancer drugs might have been identified earlier than with traditional methods.
  
  
  METHODS
  Screening algorithms used for analysis were the multi-item gamma Poisson shrinker (MGPS) and proportional reporting ratios (PRRs). Data mining was performed on data from the FDA AERS database. When a signal was identified, it was compared with that in the year in which the event was added to package insert and/or the year a "case series" was published. A recent publication summarizing the time of dissemination of information on potentially fatal AEs to cancer drugs provided the data set for analysis.
  
  
  RESULTS
  The peer-reviewed published analysis contained 21 drugs and 26 drug-event combinations (DECs) that were considered sufficiently specific for data mining. Twenty-four of the DECs generated a signal of disproportionate reporting with PRRs (6 at 1 year and 16 from 2 years to 18 years prior to either a published "case series" or a package insert change) and 20 with MGPS (3 at 1 year and 11 from 2 years to 16 years prior to either a published "case series" or a package insert change). Two DECs did not signal with either DMA.
  
  
  CONCLUSIONS
  At least one commonly cited DMA generated a signal of disproportionate reporting for 24 of 26 DECs for selected cancer drugs. For 16 DECs, one could conclude that a signal was generated well in advance (> or =2 years) of standard techniques in use with at least one DMA. DMAs might be useful in supplementing traditional surveillance strategies with oncology drugs and other drugs with similar features. (i.e., drugs that may be approved on an accelerated basis, are known to have serious toxicity, are administered to patients with substantial and complicated comorbid illness, are not available to the general medical community, and may have a high frequency of "off-label" use).
authors.affiliation: Pfizer Inc, 150 E. 42nd Street, New York, NY 10017, USA.
authors.forename: Manfred
authors.initials: M
authors.lastname: Hauben
authors.forename: Lester
authors.initials: L
authors.lastname: Reich
authors.forename: Stephanie
authors.initials: S
authors.lastname: Chung
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000465:Algorithms; D000970:Antineoplastic Agents / Q000009:adverse effects*; D017277:Drug Approval; D006801:Humans; D009026:Mortality; D017891:Pharmacoepidemiology; D011358:Product Surveillance, Postmarketing / Q000379:methods* / Q000706:statistics & numerical data; D013997:Time Factors; D014481:United States; D014486:United States Food and Drug Administration
chemical_list: D000970:Antineoplastic Agents