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I'm trying to use propensity score matching (PSM) to find matched controls. The data is not cross-sectional and it has time dependent information (monthly, quarterly, etc.). Let us say I have information from t_0 to t_N for both control units and treated units, and the event (treatment) for the treated units can happen at any time between t_0 and t_N. Is there an easy way to use propensity score matching using R or SAS to find the matched controls for each of the treated units?

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  • $\begingroup$ I don't know about easy, but if you search at Mayo Clinic website they have SAS macros for propensity score matching. $\endgroup$ – Reeza Feb 27 '17 at 23:30
  • $\begingroup$ I understand a typical propensity score matching, but I'm looking for one in the context where we have time-dependent covariates. $\endgroup$ – Ram Feb 27 '17 at 23:33
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Not matching, but you might want to look up marginal structural models, which essentially use propensity score based weights to balance treated/untreated cases on time invariant and time varying confounders.

For reference you can see:

Robins, Hernan, Brumback (2000) Marginal structural models and causal inference in epidemiology, Epidemiology, 11 (5): 550-560

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