title: Early adverse drug event signal detection within population-based health networks using sequential methods: key methodologic considerations.
abstract: OBJECTIVE
  Active surveillance of population-based health networks may improve the timeliness of detection of adverse events (AEs). Our objective was to expand our previous signal detection work by investigating the effect on signal detection of alternative study specifications.
  
  
  METHODS
  We compared the signal detection performance under various study specifications using historical data from nine health plans involved in the HMO Research Network's Center for Education and Research on Therapeutics (CERT). Five drug-event pairs representing generally accepted associations with an AE and two pairs representing "negative controls" were analyzed. Alternative study specifications related to the definition of incident users and incident AEs were assessed and compared to our previous findings.
  
  
  RESULTS
  Relaxing the incident AE exclusion criteria by (1) including members with prior outpatient diagnoses of interest and (2) halving (to 90 days) the time window specified to define incident exposure and diagnoses increased the number of members under surveillance and as a consequence increased the number of exposed days and diagnoses by about 10-20%. The alternative specifications tend to result in earlier signal detection by 10-16 months, a likely consequence of more exposures and events entering the analysis.
  
  
  CONCLUSIONS
  This paper provides additional preliminary information related to conducting prospective safety monitoring using health plan data and sequential analytic methods. Our findings support continued investigation of using health plan data and sequential analytic methods as a potentially important contribution to active drug safety surveillance.
authors.affiliation: Department of Ambulatory Care and Prevention, Harvard Medical School and Harvard Pilgrim Health Care, Boston, MA 02215, USA. jeff_brown@harvardpilgrim.org
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Brown
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
authors.forename: Kenneth R
authors.initials: KR
authors.lastname: Petronis
authors.forename: Robert
authors.initials: R
authors.lastname: Reynolds
authors.forename: K Arnold
authors.initials: KA
authors.lastname: Chan
authors.forename: Robert L
authors.initials: RL
authors.lastname: Davis
authors.forename: David
authors.initials: D
authors.lastname: Graham
authors.forename: Susan E
authors.initials: SE
authors.lastname: Andrade
authors.forename: Marsha A
authors.initials: MA
authors.lastname: Raebel
authors.forename: Lisa
authors.initials: L
authors.lastname: Herrinton
authors.forename: Douglas
authors.initials: D
authors.lastname: Roblin
authors.forename: Denise
authors.initials: D
authors.lastname: Boudreau
authors.forename: David
authors.initials: D
authors.lastname: Smith
authors.forename: Jerry H
authors.initials: JH
authors.lastname: Gurwitz
authors.forename: Margaret J
authors.initials: MJ
authors.lastname: Gunter
authors.forename: Richard
authors.initials: R
authors.lastname: Platt
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data; D016536:Competitive Medical Plans / Q000458:organization & administration / Q000706:statistics & numerical data; D064420:Drug-Related Side Effects and Adverse Reactions; D006279:Health Maintenance Organizations / Q000458:organization & administration / Q000706:statistics & numerical data; D006801:Humans; D007345:Insurance Claim Review / Q000706:statistics & numerical data; D016347:Medical Records Systems, Computerized / Q000706:statistics & numerical data; D011159:Population Surveillance / Q000379:methods*; D011358:Product Surveillance, Postmarketing / Q000379:methods* / Q000706:statistics & numerical data; D013997:Time Factors; D016896:Treatment Outcome; D014481:United States