title: A pharmacoepidemiological network model for drug safety surveillance: statins and rhabdomyolysis.
abstract: BACKGROUND
  Recent withdrawals of major drugs have highlighted the critical importance of drug safety surveillance in the postmarketing phase. Limitations of spontaneous report data have led drug safety professionals to pursue alternative postmarketing surveillance approaches based on healthcare administrative claims data. These data are typically analysed by comparing the adverse event rates associated with a drug of interest to those of a single comparable reference drug.
  
  
  OBJECTIVE
  The aim of this study was to determine whether adverse event detection can be improved by incorporating information from multiple reference drugs. We developed a pharmacological network model that implemented this approach and evaluated its performance.
  
  
  METHODS
  We studied whether adverse event detection can be improved by incorporating information from multiple reference drugs, and describe two approaches for doing so. The first, reported previously, combines a set of related drugs into a single reference cohort. The second is a novel pharmacoepidemiological network model, which integrates multiple pair-wise comparisons across an entire set of related drugs into a unified consensus safety score for each drug. We also implemented a single reference drug approach for comparison with both multi-drug approaches. All approaches were applied within a sequential analysis framework, incorporating new information as it became available and addressing the issue of multiple testing over time. We evaluated all these approaches using statin (HMG-CoA reductase inhibitors) safety data from a large healthcare insurer in the US covering April 2000 through March 2005.
  
  
  RESULTS
  We found that both multiple reference drug approaches offer earlier detection (6-13 months) than the single reference drug approach, without triggering additional false positives.
  
  
  CONCLUSIONS
  Such combined approaches have the potential to be used with existing healthcare databases to improve the surveillance of therapeutics in the postmarketing phase over single-comparator methods. The proposed network approach also provides an integrated visualization framework enabling decision makers to understand the key high-level safety relationships amongst a group of related drugs.
authors.affiliation: Childrens Hospital Informatics Program, Harvard-MIT Division of Health Sciences and Technology, Childrens Hospital, Harvard Medical School, Boston, MA, USA.
authors.forename: Ben Y
authors.initials: BY
authors.lastname: Reis
authors.forename: Karen L
authors.initials: KL
authors.lastname: Olson
authors.forename: Lu
authors.initials: L
authors.lastname: Tian
authors.forename: Rhonda L
authors.initials: RL
authors.lastname: Bohn
authors.forename: John S
authors.initials: JS
authors.lastname: Brownstein
authors.forename: Peter J
authors.initials: PJ
authors.lastname: Park
authors.forename: Mark J
authors.initials: MJ
authors.lastname: Cziraky
authors.forename: Marcus D
authors.initials: MD
authors.lastname: Wilson
authors.forename: Kenneth D
authors.initials: KD
authors.lastname: Mandl
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards*; D015331:Cohort Studies; D016903:Drug Monitoring / Q000379:methods; D005260:Female; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000009:adverse effects*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D017891:Pharmacoepidemiology / Q000379:methods*; D012189:Retrospective Studies; D012206:Rhabdomyolysis / Q000139:chemically induced*; D012307:Risk Factors; D014481:United States
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors