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10.3238/arztebl.2016.0597 http://scihub22266oqcxt.onion/10.3238/arztebl.2016.0597 C5963493!5963493!27658473     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=27658473&cmd=llinks): Failed to open stream: HTTP request failed! 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Propensity Score: an Alternative Method of Analyzing Treatment Effects: Part 23 of a Series on Evaluation of Scientific Publications |pdf=|usr=}}{{27658473}} gab.com Text Propensity Score: an Alternative Method of Analyzing Treatment Effects: Part 23 of a Series on Evaluation of Scientific Publications JO: Dtsch Arztebl Int http://www.kidney.de/pget.php?&tw=1&pmid=27658473 #FOAvip 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 search 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. Twit Text FOAVip Propensity Score: an Alternative Method of Analyzing Treatment Effects: Part 23 of a Series on Evaluation of Scientific Publications ????Dtsch Arztebl Int http://www.kidney.de/pget.php?&tw=1&pmid=27658473 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=27658473 #FOAvip English Wikipedia <ref>{{Cite pmid|27658473}}</ref> Propensity Score: an Alternative Method of Analyzing Treatment Effects: Part 23 of a Series on Evaluation of Scientific Publications #MMPMID27658473 Kuss O; Blettner M; Börgermann J Dtsch Arztebl Int 2016[Sep]; 113 (35-36): 597-603 PMID27658473show ga 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 search 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. äPropensity Score: an Alternative Method of Analyzing Treatment Effects(epmc).pdf DeepDyve Pubget Overpricing