Propensity scores are a statistical method for adjusting for baseline differences between study groups. The scores are based on the probability of a subject being in a particular group, conditional on that subject’s values on those independent variables though to influence group membership. Propensity scores with multivariable analysis produces a better adjustment for baseline differences than simply including potential confounders in a multivariable model predicting outcome. Propensity scores are also particularly helpful when outcomes are rare and the proportions of subjects in the independent groups are relatively equal. Another advantage of propensity scores is that they make no assumptions about the relationship between the individual confounders and outcome. The adequacy of a propensity score is judged by whether there is sufficient overlap between the groups and whether it balances the covariates.

There are four major ways you can use propensity scores: matching, weighting, stratified, as a covariate in a model predicting outcome.