Statistical primer: Propensity score matching and its alternatives

Umberto Benedetto*, Stuart J. Head, Gianni D. Angelini, Eugene H. Blackstone

*Corresponding author for this work

Research output: Contribution to journalReview article (Academic Journal)

34 Citations (Scopus)
187 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)1112-1117
Number of pages6
JournalEuropean Journal of Cardio-Thoracic Surgery
Volume53
Issue number6
DOIs
Publication statusPublished - 1 Jun 2018

Structured keywords

  • Centre for Surgical Research

Keywords

  • Matching
  • Propensity score
  • Statistics
  • Stratification
  • Weighting

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