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National Cardiac Societies document reviewers: listed in Addenda The content of these European Society of Cardiology (ESC) and European Association for Cardio-Thoracic Surgery (EACTS) Guidelines has been published for personal and educational use only. No commercial use is authorized. No part of the ESC Guidelines may be translated or reproduced in any form without written permission from the ESC. Permission can be obtained upon submission of a written request to Oxford University Press, the publisher of the European Heart Journal and the party authorized to handle such permissions on behalf of the ESC.

Członków Komisji Europejskiego Towarzystwa Kardiologicznego ds. Wytycznych dotyczących Praktyki Klinicznej (CPG), Komisji Europejskiego Stowarzyszenia Chirurgii Serca i Klatki Piersiowej ds. Wytycznych (EACTS Clinical Guidelines Committee) oraz recenzentów dokumentu ze strony narodowych towarzystw kardiologicznych wchodzących w skład ESC wymieniono w Dodatku.

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.