Calculate the effect estimate and standard errors with this match population. Check the balance of covariates in the exposed and unexposed groups after matching on PS.ĥ. Match exposed and unexposed subjects on the PS.Ĥ. Use logistic regression to obtain a PS for each subject.ģ. Decide on the set of covariates you want to include.Ģ. PSA helps us to mimic an experimental study using data from an observational study. But we still would like the exchangeability of groups achieved by randomization. There are several occasions where an experimental study is not feasible or ethical.

One of the biggest challenges with observational studies is that the probability of being in the exposed or unexposed group is not random. Therefore, we say that we have exchangeability between groups. This equal probability of exposure makes us feel more comfortable asserting that the exposed and unexposed groups are alike on all factors except their exposure. Therefore, a subject’s actual exposure status is random. The probability of being exposed or unexposed is the same. Thus, the probability of being unexposed is also 0.5. randomized control trials), the probability of being exposed is 0.5. Exchangeability is critical to our causal inference. Propensity score analysis (PSA) arose as a way to achieve exchangeability between exposed and unexposed groups in observational studies without relying on traditional model building. Thus, the probability of being exposed is the same as the probability of being unexposed. We can match exposed subjects with unexposed subjects with the same (or very similar) PS. Once we have a PS for each subject, we then return to the real world of exposed and unexposed. We can calculate a PS for each subject in an observational study regardless of her actual exposure. In fact, it is a conditional probability of being exposed given a set of covariates, Pr(E+|covariates).

Propensity score matching using xlstat