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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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Autores principales: Bergstra, Sytske Anne, Sepriano, Alexandre, Ramiro, Sofia, Landewé, Robert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525599/
https://www.ncbi.nlm.nih.gov/pubmed/31168417
http://dx.doi.org/10.1136/rmdopen-2019-000953
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author Bergstra, Sytske Anne
Sepriano, Alexandre
Ramiro, Sofia
Landewé, Robert
author_facet Bergstra, Sytske Anne
Sepriano, Alexandre
Ramiro, Sofia
Landewé, Robert
author_sort Bergstra, Sytske Anne
collection PubMed
description 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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spelling pubmed-65255992019-06-05 Three handy tips and a practical guide to improve your propensity score models Bergstra, Sytske Anne Sepriano, Alexandre Ramiro, Sofia Landewé, Robert RMD Open Epidemiology 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. BMJ Publishing Group 2019-05-01 /pmc/articles/PMC6525599/ /pubmed/31168417 http://dx.doi.org/10.1136/rmdopen-2019-000953 Text en © Author(s) (or their employer(s)) 2019. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.
spellingShingle Epidemiology
Bergstra, Sytske Anne
Sepriano, Alexandre
Ramiro, Sofia
Landewé, Robert
Three handy tips and a practical guide to improve your propensity score models
title Three handy tips and a practical guide to improve your propensity score models
title_full Three handy tips and a practical guide to improve your propensity score models
title_fullStr Three handy tips and a practical guide to improve your propensity score models
title_full_unstemmed Three handy tips and a practical guide to improve your propensity score models
title_short Three handy tips and a practical guide to improve your propensity score models
title_sort three handy tips and a practical guide to improve your propensity score models
topic Epidemiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6525599/
https://www.ncbi.nlm.nih.gov/pubmed/31168417
http://dx.doi.org/10.1136/rmdopen-2019-000953
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