title: Efficient sampling approaches to address confounding in database studies.
abstract: Administrative and other population-based databases are widely used in pharmacoepidemiology to study the unintended effects of medications. They allow investigators to study large case series, and they document prescription medication exposure without having to contact individuals or medical charts, or rely on human recall. However, such databases often lack information on potentially important confounding variables. This review describes some of the sampling approaches and accompanying data-analysis methods that can be used to assess, and deal efficiently with, such confounding.
authors.affiliation: Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada. james.hanley@mcgill.ca
authors.forename: James A
authors.initials: JA
authors.lastname: Hanley
authors.forename: Nandini
authors.initials: N
authors.lastname: Dendukuri
mesh_terms: D015986:Confounding Factors, Epidemiologic; D019992:Databases as Topic / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D017891:Pharmacoepidemiology / Q000706:statistics & numerical data*; D012494:Sampling Studies