%0 Journal Article %1 Kuss2016 %A Kuss, Oliver %A Blettner, Maria %A Börgermann, Jochen %D 2016 %J Deutsches Arzteblatt international %K imported %N 35-36 %P 597-603 %R 10.3238/arztebl.2016.0597 %T Propensity Score: an Alternative Method of Analyzing Treatment Effects. %U http://www.ncbi.nlm.nih.gov/pubmed/27658473 %V 113 %X 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. CONCLUSION 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.

@article{Kuss2016, 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. CONCLUSION 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.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Kuss, Oliver and Blettner, Maria and Börgermann, Jochen}, biburl = {https://www.bibsonomy.org/bibtex/2628c9a5b51f370815dda212e29a9c09b/jepcastel}, doi = {10.3238/arztebl.2016.0597}, interhash = {1a79751613bb01d83b935c8068ce26eb}, intrahash = {628c9a5b51f370815dda212e29a9c09b}, issn = {1866-0452}, journal = {Deutsches Arzteblatt international}, keywords = {imported}, month = {9}, note = {Propensity score; Introductori<br/><br/>Bona figura amb diferents aplicacions del PS}, number = {35-36}, pages = {597-603}, pmid = {27658473}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Propensity Score: an Alternative Method of Analyzing Treatment Effects.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/27658473}, volume = 113, year = 2016 }