title: [Propensity score methods for creating covariate balance in observational studies].
abstract: Randomization of treatment assignment in experiments generates treatment groups with approximately balanced baseline covariates. However, in observational studies, where treatment assignment is not random, patients in the active treatment and control groups often differ on crucial covariates that are related to outcomes. These covariate imbalances can lead to biased treatment effect estimates. The propensity score is the probability that a patient with particular baseline characteristics is assigned to active treatment rather than control. Though propensity scores are unknown in observational studies, by matching or subclassifying patients on estimated propensity scores, we can design observational studies that parallel randomized experiments, with approximate balance on observed covariates. Observational study designs based on estimated propensity scores can generate approximately unbiased treatment effect estimates. Critically, propensity score designs should be created without access to outcomes, mirroring the separation of study design and outcome analysis in randomized experiments. This paper describes the potential outcomes framework for causal inference and best practices for designing observational studies with propensity scores. We discuss the use of propensity scores in two studies assessing the effectiveness and risks of antifibrinolytic drugs during cardiac surgery.
authors.affiliation: Department of Statistics, Harvard University, Cambridge, Massachusetts, USA.
authors.forename: Cassandra W
authors.initials: CW
authors.lastname: Pattanayak
authors.forename: Donald B
authors.initials: DB
authors.lastname: Rubin
authors.forename: Elizabeth R
authors.initials: ER
authors.lastname: Zell
mesh_terms: D000704:Analysis of Variance; D000933:Antifibrinolytic Agents / Q000627:therapeutic use; D007611:Aprotinin / Q000627:therapeutic use; D015984:Causality; D015986:Confounding Factors, Epidemiologic; D003627:Data Interpretation, Statistical; D006490:Hemostatics / Q000627:therapeutic use; D006801:Humans; D057216:Propensity Score; D011897:Random Allocation; D016032:Randomized Controlled Trials as Topic / Q000706:statistics & numerical data*; D012044:Regression Analysis; D014148:Tranexamic Acid / Q000627:therapeutic use; D016896:Treatment Outcome
chemical_list: D000933:Antifibrinolytic Agents; D006490:Hemostatics; D014148:Tranexamic Acid; D007611:Aprotinin