Abstract
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.
| Original language | English |
|---|---|
| Article number | e000953 |
| Journal | RMD Open |
| Volume | 5 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 1 Apr 2019 |
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Keywords
- bias
- observational studies
- propensity scores
- treatment effects
Cite this
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Three handy tips and a practical guide to improve your propensity score models. / Bergstra, Sytske Anne; Sepriano, Alexandre; Ramiro, Sofia; Landewé, Robert.
In: RMD Open, Vol. 5, No. 1, e000953, 01.04.2019.Research output: Contribution to journal › Review article
TY - JOUR
T1 - Three handy tips and a practical guide to improve your propensity score models
AU - Bergstra, Sytske Anne
AU - Sepriano, Alexandre
AU - Ramiro, Sofia
AU - Landewé, Robert
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - bias
KW - observational studies
KW - propensity scores
KW - treatment effects
UR - http://www.scopus.com/inward/record.url?scp=85065299275&partnerID=8YFLogxK
U2 - 10.1136/rmdopen-2019-000953
DO - 10.1136/rmdopen-2019-000953
M3 - Review article
VL - 5
JO - RMD Open
JF - RMD Open
SN - 2044-6055
IS - 1
M1 - e000953
ER -