title: Propensity Score: an Alternative Method of Analyzing Treatment Effects.
abstract: BACKGROUND
  In intervention trials, only randomization guarantees equal distributions of all known and unknown patient characteristics between an intervention group and a control group and enables causal statements on treatment effects. However, randomized controlled trials have been criticized for insufficient external validity; non-randomized trials are an alternative here, but come with the danger of intervention and control groups differing with respect to known and/or unknown patient characteristics. Non-randomized trials are generally analyzed with multiple regression models, but the so-called propensity score method is now being increasingly used.
  
  
  METHODS
  The authors present, explain, and illustrate the propensity score method, using a study on coronary artery bypass surgery as an illustrative example. This article is based on publications retrieved by a selective literature earch and on the authors' scientific experience.
  
  
  RESULTS
  The propensity score (PS) is defined as the probability that a patient will receive the treatment under investigation. In a first step, the PS is estimated from the available data, e.g. in a logistic regression model. In a second step, the actual treatment effect is estimated with the aid of the PS. Four methods are available for this task: PS matching, inverse probability of treatment weighting (IPTW), stratification by PS, and regression adjustment for the PS.
  
  
  CONCLUSIONS
  The propensity score method is a good alternative method for the analysis of non-randomized intervention trials, with epistemological advantages over conventional regression modelling. Nonetheless, the propensity score method can only adjust for known confounding factors that have actually been measured. Equal distributions of unknown confounding factors can be achieved only in randomized controlled trials.
authors.affiliation: German Diabetes Center, Institute for Biometrics and Epidemiology and Centre for Health and Society (chs), Heinrich-Heine-Universität Düsseldorf, Institute for Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center Mainz, Department of Cardiothoracic Surgery, Heart and Diabetes Center North Rhine-Westphalia, Ruhr-University Bochum, Bad Oeynhausen.
authors.forename: Oliver
authors.initials: O
authors.lastname: Kuss
authors.forename: Maria
authors.initials: M
authors.lastname: Blettner
authors.forename: Jochen
authors.initials: J
authors.lastname: Börgermann
mesh_terms: D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D019317:Evidence-Based Medicine / Q000379:methods*; D015233:Models, Statistical; D017063:Outcome Assessment, Health Care / Q000379:methods*; D057216:Propensity Score; D016032:Randomized Controlled Trials as Topic / Q000379:methods*; D016896:Treatment Outcome