title: A review of the application of propensity score methods yielded increasing use, advantages in specific settings, but not substantially different estimates compared with conventional multivariable methods.
abstract: OBJECTIVE
  Propensity score (PS) analyses attempt to control for confounding in nonexperimental studies by adjusting for the likelihood that a given patient is exposed. Such analyses have been proposed to address confounding by indication, but there is little empirical evidence that they achieve better control than conventional multivariate outcome modeling.
  
  
  METHODS
  Using PubMed and Science Citation Index, we assessed the use of propensity scores over time and critically evaluated studies published through 2003.
  
  
  RESULTS
  Use of propensity scores increased from a total of 8 reports before 1998 to 71 in 2003. Most of the 177 published studies abstracted assessed medications (N=60) or surgical interventions (N=51), mainly in cardiology and cardiac surgery (N=90). Whether PS methods or conventional outcome models were used to control for confounding had little effect on results in those studies in which such comparison was possible. Only 9 of 69 studies (13%) had an effect estimate that differed by more than 20% from that obtained with a conventional outcome model in all PS analyses presented.
  
  
  CONCLUSIONS
  Publication of results based on propensity score methods has increased dramatically, but there is little evidence that these methods yield substantially different estimates compared with conventional multivariable methods.
authors.affiliation: Division of Pharmacoepidemiology and Pharmacoeconomics, Brigham and Women's Hospital, Harvard Medical School, 1620 Tremont Street, Suite 3030, Boston, MA 02120, USA. til.sturmer@post.harvard.edu
authors.forename: Til
authors.initials: T
authors.lastname: Stürmer
authors.forename: Manisha
authors.initials: M
authors.lastname: Joshi
authors.forename: Robert J
authors.initials: RJ
authors.lastname: Glynn
authors.forename: Jerry
authors.initials: J
authors.lastname: Avorn
authors.forename: Kenneth J
authors.initials: KJ
authors.lastname: Rothman
authors.forename: Sebastian
authors.initials: S
authors.lastname: Schneeweiss
mesh_terms: D015982:Bias; D006348:Cardiac Surgical Procedures / Q000379:methods; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D016206:Databases, Bibliographic; D004812:Epidemiologic Methods; D006331:Heart Diseases / Q000188:drug therapy; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D016896:Treatment Outcome