Propensity score matching for estimation of pairwise marginal hazard ratios

There is a growing interest in using observational studies to estimate the effects of treatments on survival or time-to-event outcomes. However, few standard approaches can adequately accommodate multiple treatment levels, which are common in observational comparative effectiveness research. We study the asymptotic properties of the generalized propensity score matching estimators of the marginal hazard ratios between pairs of treatment levels. The estimates are obtained by fitting a marginal Cox proportional hazard model on the matched dataset. We evaluate our approach in a simulation study and a case study where we analyze the IQVIA electronic medical records data.