title: The validity of sequence symmetry analysis (SSA) for adverse drug reaction signal detection.
abstract: OBJECTIVE
  To determine the validity of sequence symmetry analysis (SSA) method to detect adverse drug reactions from an administrative claims database.
  
  
  METHODS
  Published randomised controlled trials (RCTs) of 19 medicines were identified through search databases, product information (PI) or the US Food and Drug Administration Web site. All adverse events (AEs) in the RCTs and the PI for the medicines were extracted. AEs were considered 'gold standard positive events' if they were reported as being statistically significant events in adequately powered RCTs. The remaining AEs were considered 'gold standard negative events' if the event was not listed as an AE in the PI for that medicine or any other medicine in its class. Indicators of AEs were identified by consensus from two clinical researchers. SSA was run for each medicine-indicator pair using four different time windows: 3, 6, 9 and 12 months.
  
  
  RESULTS
  A total of 120 randomised placebo controlled trials were reviewed for the 19 tested medicines. A total of 165 medicine-indicator pairs (44 positive and 121 negative events) were identified and tested by SSA. At the 12-month time window, the sensitivity, specificity, positive and negative predictive values of SSA were 61% (95%CI 0.46-0.74), 93% (95%CI 0.87-0.96), 77% (95%CI 0.61-0.88) and 87% (95%CI 0.80-0.92), respectively. Using a 3-month time window, the SSA had a lower sensitivity (52%).
  
  
  CONCLUSIONS
  The SSA technique was found to have moderate sensitivity and high specificity for detecting ADRs. These results suggest that SSA is a potential tool for detecting ADRs using administrative claims data that could complement existing pharmacosurveillance methods.
authors: School of Pharmacy and Medical Sciences, Quality Use of Medicines and Pharmacy Research Centre, Sansom Institute, University of South Australia, Adelaide, South Australia, Australia.
authors: Izyan A
authors: IA
authors: Wahab
authors: Nicole L
authors: NL
authors: Pratt
authors: Michael D
authors: MD
authors: Wiese
authors: Lisa M
authors: LM
authors: Kalisch
authors: Elizabeth E
authors: EE
authors: Roughead
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D060735:Pharmacovigilance; D011237:Predictive Value of Tests; D016032:Randomized Controlled Trials as Topic; D012680:Sensitivity and Specificity; D013997:Time Factors; D014481:United States; D014486:United States Food and Drug Administration