![]() In this viewpoint we will discuss the most commonly encountered flaws and provide a stepwise description on the estimation and use of PS, such that in future publications these flaws can be avoided. These include perfect prediction of treatment allocation, untied observations and lack of generalisability due to oversimplification of complex clinical scenarios. But researchers using PS sometimes fail to recognise important methodological flaws which can lead to spurious conclusions. At least in theory, in patients with similar PS, the treatment prescribed will be independent of these variables (pseudorandomisation). The PS is a score between 0 and 1 that reflects the likelihood per patient of receiving one of the treatment categories of interest conditional on a set of variables. A popular method to adjust for this type of bias is the use of propensity scores (PS). However, since treatment in observational studies is not randomly allocated, confounding by indication may occur, in which differences in patient characteristics may influence both treatment choices and treatment responses. Real-world data are increasingly available to investigate ‘real-world’ safety and efficacy.
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