title: Drug safety data mining with a tree-based scan statistic.
abstract: OBJECTIVE
  In post-marketing drug safety surveillance, data mining can potentially detect rare but serious adverse events. Assessing an entire collection of drug-event pairs is traditionally performed on a predefined level of granularity. It is unknown a priori whether a drug causes a very specific or a set of related adverse events, such as mitral valve disorders, all valve disorders, or different types of heart disease. This methodological paper evaluates the tree-based scan statistic data mining method to enhance drug safety surveillance.
  
  
  METHODS
  We use a three-million-member electronic health records database from the HMO Research Network. Using the tree-based scan statistic, we assess the safety of selected antifungal and diabetes drugs, simultaneously evaluating overlapping diagnosis groups at different granularity levels, adjusting for multiple testing. Expected and observed adverse event counts were adjusted for age, sex, and health plan, producing a log likelihood ratio test statistic.
  
  
  RESULTS
  Out of 732 evaluated disease groupings, 24 were statistically significant, divided among 10 non-overlapping disease categories. Five of the 10 signals are known adverse effects, four are likely due to confounding by indication, while one may warrant further investigation.
  
  
  CONCLUSIONS
  The tree-based scan statistic can be successfully applied as a data mining tool in drug safety surveillance using observational data. The total number of statistical signals was modest and does not imply a causal relationship. Rather, data mining results should be used to generate candidate drug-event pairs for rigorous epidemiological studies to evaluate the individual and comparative safety profiles of drugs.
authors.affiliation: Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. martin_kulldorff@hms.harvard.edu
authors.forename: Martin
authors.initials: M
authors.lastname: Kulldorff
authors.forename: Inna
authors.initials: I
authors.lastname: Dashevsky
authors.forename: Taliser R
authors.initials: TR
authors.lastname: Avery
authors.forename: Arnold K
authors.initials: AK
authors.lastname: Chan
authors.forename: Robert L
authors.initials: RL
authors.lastname: Davis
authors.forename: David
authors.initials: D
authors.lastname: Graham
authors.forename: Richard
authors.initials: R
authors.lastname: Platt
authors.forename: Susan E
authors.initials: SE
authors.lastname: Andrade
authors.forename: Denise
authors.initials: D
authors.lastname: Boudreau
authors.forename: Margaret J
authors.initials: MJ
authors.lastname: Gunter
authors.forename: Lisa J
authors.initials: LJ
authors.lastname: Herrinton
authors.forename: Pamala A
authors.initials: PA
authors.lastname: Pawloski
authors.forename: Marsha A
authors.initials: MA
authors.lastname: Raebel
authors.forename: Douglas
authors.initials: D
authors.lastname: Roblin
authors.forename: Jeffrey S
authors.initials: JS
authors.lastname: Brown
mesh_terms: D000328:Adult; D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D000368:Aged; D000935:Antifungal Agents / Q000009:adverse effects*; D057225:Data Mining / Q000379:methods*; D016208:Databases, Factual / Q000706:statistics & numerical data; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D006279:Health Maintenance Organizations; D006801:Humans; D007004:Hypoglycemic Agents / Q000009:adverse effects*; D016013:Likelihood Functions; D008297:Male; D008875:Middle Aged; D017891:Pharmacoepidemiology / Q000379:methods; D060735:Pharmacovigilance; D011358:Product Surveillance, Postmarketing / Q000379:methods; D055815:Young Adult
chemical_list: D000935:Antifungal Agents; D007004:Hypoglycemic Agents