To adjust for confounding by indication, the PS can be used for stratified sampling, matching or as a covariate in regression analyses. At least in theory, in patients with similar PS, the treatment prescribed will be independent of the added variables (pseudorandomisation).
This likelihood is estimated by binomial or polynomial regression analysis and is conditional on a set of pretreatment variables that together reflect to some extent the factors the prescriber considers when making a treatment choice, and that at the same time influence the outcome (eg, disease activity, physical functioning, imaging findings, and so on). The PS is a score between 0 and 1 that reflects the likelihood per patient of receiving one of the treatment categories of interest. An increasingly popular method to address this is the use of propensity scores (PS). Therefore, crude comparisons between treatment effects are insufficient and methods should be applied to adjust for this bias, in order to obtain valid results. This means that differences in patient characteristics that are predictive of disease severity may guide both treatment choices as well as treatment responses and may thus lead to confounding by indication. In other words, a specific patient may receive a specific treatment (and not another one) due to some specific personal or disease characteristics. However, treatment in observational studies is not randomly allocated. Real-world data are almost routinely collected in rheumatology and are now available to investigate ‘real-world’ safety and efficacy of medical interventions. 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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