title: An application of propensity score matching using claims data.
abstract: BACKGROUND
  Confounding by indication is a common problem in pharmacoepidemiology, where predictors of treatment also have prognostic value for the outcome of interest. The tools available to the epidemiologist that can be used to mitigate the effects of confounding by indication often have limits with respect to the number of variables that can be simultaneously incorporated as components of the confounding. This constraint becomes particularly apparent in the context of a rich data source (such as administrative claims data), applied to the study of an outcome that occurs infrequently. In such settings, there will typically be many more variables available for control as potential confounders than traditional epidemiologic techniques will allow.
  
  
  METHODS
  One tool that can indirectly permit control of a large number of variables is the propensity score approach. This paper illustrates the application of the propensity score to a study conducted in an administrative database, and raises critical issues to be addressed in such an analysis. In this example, the effect of statin therapy on the occurrence of myocardial infarction was examined, and numerous potential confounders of this association were adjusted simultaneously using a propensity score to form matched cohorts of statin initiators and non-initiators.
  
  
  RESULTS
  The incidence of myocardial infarction observed in the statin treated cohort was lower than the incidence in the untreated cohort, and the magnitude of this effect was consistent with results from randomized placebo controlled clinical trials of statin therapy.
  
  
  CONCLUSIONS
  This example illustrates how confounding by indication can be mitigated by the propensity score matching technique. Concerns remain over the generalizability of estimates obtained from such a study, and how to know when propensity scores are removing bias, since apparent balance between compared groups on measured variables could leave variables not included in the propensity score unbalanced and lead to confounded effect estimates.
authors.affiliation: Ingenix Epidemiology, Auburndale, Massachusetts 02466, USA. jseeger@epidemiology.com
authors.forename: John D
authors.initials: JD
authors.lastname: Seeger
authors.forename: Paige L
authors.initials: PL
authors.lastname: Williams
authors.forename: Alexander M
authors.initials: AM
authors.lastname: Walker
mesh_terms: D015331:Cohort Studies; D015986:Confounding Factors, Epidemiologic; D003625:Data Collection / Q000379:methods; D006801:Humans; D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors / Q000627:therapeutic use*; D006937:Hypercholesterolemia / Q000188:drug therapy* / Q000453:epidemiology; D015994:Incidence; D009203:Myocardial Infarction / Q000453:epidemiology / Q000517:prevention & control*; D017063:Outcome Assessment, Health Care; D010593:Pharmaceutical Services / Q000706:statistics & numerical data*; D017891:Pharmacoepidemiology / Q000379:methods*; D016032:Randomized Controlled Trials as Topic; D012307:Risk Factors; D013223:Statistics as Topic
chemical_list: D019161:Hydroxymethylglutaryl-CoA Reductase Inhibitors