Three handy tips and a practical guide to improve your propensity score models
Real-world data are increasingly available to investigate ‘real-world’ safety and efficacy. 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 treat...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMJ Publishing Group
2019-06-01
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| Series: | RMD Open |
| Online Access: | https://rmdopen.bmj.com/content/5/1/e000953.full |
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| Summary: | Real-world data are increasingly available to investigate ‘real-world’ safety and efficacy. 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. A popular method to adjust for this type of bias is the use of propensity scores (PS). 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. At least in theory, in patients with similar PS, the treatment prescribed will be independent of these variables (pseudorandomisation). But researchers using PS sometimes fail to recognise important methodological flaws which can lead to spurious conclusions. These include perfect prediction of treatment allocation, untied observations and lack of generalisability due to oversimplification of complex clinical scenarios. 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. |
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| ISSN: | 2056-5933 |