Statistical primer: propensity score matching and its alternatives
Propensity score (PS) methods offer certain advantages over more traditional regression methods to control for confounding by indication in observational studies. Although multivariable regression models adjust for confounders by modelling the relationship between covariates and outcome, the PS meth...
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| Published in | European journal of cardio-thoracic surgery Vol. 53; no. 6; pp. 1112 - 1117 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Germany
01.06.2018
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Propensity score (PS) methods offer certain advantages over more traditional regression methods to control for confounding by indication in observational studies. Although multivariable regression models adjust for confounders by modelling the relationship between covariates and outcome, the PS methods estimate the treatment effect by modelling the relationship between confounders and treatment assignment. Therefore, methods based on the PS are not limited by the number of events, and their use may be warranted when the number of confounders is large, or the number of outcomes is small. The PS is the probability for a subject to receive a treatment conditional on a set of baseline characteristics (confounders). The PS is commonly estimated using logistic regression, and it is used to match patients with similar distribution of confounders so that difference in outcomes gives unbiased estimate of treatment effect. This review summarizes basic concepts of the PS matching and provides guidance in implementing matching and other methods based on the PS, such as stratification, weighting and covariate adjustment. |
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| AbstractList | Propensity score (PS) methods offer certain advantages over more traditional regression methods to control for confounding by indication in observational studies. Although multivariable regression models adjust for confounders by modelling the relationship between covariates and outcome, the PS methods estimate the treatment effect by modelling the relationship between confounders and treatment assignment. Therefore, methods based on the PS are not limited by the number of events, and their use may be warranted when the number of confounders is large, or the number of outcomes is small. The PS is the probability for a subject to receive a treatment conditional on a set of baseline characteristics (confounders). The PS is commonly estimated using logistic regression, and it is used to match patients with similar distribution of confounders so that difference in outcomes gives unbiased estimate of treatment effect. This review summarizes basic concepts of the PS matching and provides guidance in implementing matching and other methods based on the PS, such as stratification, weighting and covariate adjustment. Propensity score (PS) methods offer certain advantages over more traditional regression methods to control for confounding by indication in observational studies. Although multivariable regression models adjust for confounders by modelling the relationship between covariates and outcome, the PS methods estimate the treatment effect by modelling the relationship between confounders and treatment assignment. Therefore, methods based on the PS are not limited by the number of events, and their use may be warranted when the number of confounders is large, or the number of outcomes is small. The PS is the probability for a subject to receive a treatment conditional on a set of baseline characteristics (confounders). The PS is commonly estimated using logistic regression, and it is used to match patients with similar distribution of confounders so that difference in outcomes gives unbiased estimate of treatment effect. This review summarizes basic concepts of the PS matching and provides guidance in implementing matching and other methods based on the PS, such as stratification, weighting and covariate adjustment.Propensity score (PS) methods offer certain advantages over more traditional regression methods to control for confounding by indication in observational studies. Although multivariable regression models adjust for confounders by modelling the relationship between covariates and outcome, the PS methods estimate the treatment effect by modelling the relationship between confounders and treatment assignment. Therefore, methods based on the PS are not limited by the number of events, and their use may be warranted when the number of confounders is large, or the number of outcomes is small. The PS is the probability for a subject to receive a treatment conditional on a set of baseline characteristics (confounders). The PS is commonly estimated using logistic regression, and it is used to match patients with similar distribution of confounders so that difference in outcomes gives unbiased estimate of treatment effect. This review summarizes basic concepts of the PS matching and provides guidance in implementing matching and other methods based on the PS, such as stratification, weighting and covariate adjustment. |
| Author | Benedetto, Umberto Head, Stuart J Blackstone, Eugene H Angelini, Gianni D |
| Author_xml | – sequence: 1 givenname: Umberto surname: Benedetto fullname: Benedetto, Umberto organization: Bristol Heart Institute, University of Bristol, School of Clinical Sciences, Bristol, UK – sequence: 2 givenname: Stuart J surname: Head fullname: Head, Stuart J organization: Department of Cardiothoracic Surgery, Erasmus Medical Center, Rotterdam, Netherlands – sequence: 3 givenname: Gianni D surname: Angelini fullname: Angelini, Gianni D organization: Bristol Heart Institute, University of Bristol, School of Clinical Sciences, Bristol, UK – sequence: 4 givenname: Eugene H surname: Blackstone fullname: Blackstone, Eugene H organization: Department of Thoracic and Cardiovascular Surgery & Clinical Investigations, Cleveland Clinic Foundation, Cleveland, OH, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/29684154$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1056/NEJM200006223422511 10.1093/aje/kwj149 10.1002/pds.2074 10.2307/2533160 10.1002/sim.3150 10.1093/jnci/djw323 10.1067/mtc.2002.120329 10.1002/sim.5753 10.1002/sim.3549 10.1002/sim.5705 10.1080/00273171.2011.568786 10.1002/sim.3697 10.1002/sim.2739 10.1214/09-STS313 10.1002/sim.2781 10.2307/2528036 10.1136/bmj.39335.541782.AD 10.1002/sim.6004 10.1002/sim.4200 |
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| References | von Elm ( key 20180517094621_ezy167-B19) 2007; 335 Blackstone ( key 20180517094621_ezy167-B15) 2002; 123 Rubin ( key 20180517094621_ezy167-B2) 1996; 52 Rubin ( key 20180517094621_ezy167-B5) 2007; 26 Westreich ( key 20180517094621_ezy167-B10) 2011; 20 Austin ( key 20180517094621_ezy167-B7) 2007; 26 Brookhart ( key 20180517094621_ezy167-B9) 2006; 163 Austin ( key 20180517094621_ezy167-B11) 2014; 33 McCaffrey ( key 20180517094621_ezy167-B13) 2013; 32 Qu ( key 20180517094621_ezy167-B17) 2009; 28 Austin ( key 20180517094621_ezy167-B3) 2011; 46 Cochran ( key 20180517094621_ezy167-B12) 1968; 24 Austin ( key 20180517094621_ezy167-B6) 2008; 27 Yao ( key 20180517094621_ezy167-B18) 2017; 109 Stuart ( key 20180517094621_ezy167-B4) 2010; 25 Austin ( key 20180517094621_ezy167-B8) 2013; 32 Austin ( key 20180517094621_ezy167-B16) 2011; 30 Pocock ( key 20180517094621_ezy167-B1) 2000; 342 Austin ( key 20180517094621_ezy167-B14) 2009; 28 |
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