It is becoming increasingly common that clinical researchers are designing longitudinal observational studies, enabling multiple treatment efficacy and safety comparison that would not have been observable in clinical trials. To adjust for time-dependent confounding, two techniques have been widely adopted, propensity score (PS) and inverse probability of treatment weighting (IPTW). Bayesian casual inference can incorporate prior clinical beliefs about treatment effectiveness, return probabilistic summaries and propagate PS estimate uncertainty. Existing Bayesian methods for longitudinal data include Bayesian estimation of marginal structure models (MSM) on an end-of-study outcome, and generalized propensity score (GPS) for dose-response studies. Limited literature explored Bayesian causal inference with repeatedly measured outcomes. In this paper, we explore Bayesian estimation of MSM with explicitly defined repeated outcome measurements. Our proposed method permits causal estimation of the treatment effect at each time point. Time dependent IPTW are obtained from MCMC samples of the posterior predictive treatment assignment model at each follow-up visit. We use a simulation study to compare the proposed method with existing methods. This is a joint work with Dr. Olli Saarela, the University of Toronto and Dr. Eleanor Pullenayegum, the Hospital for Sick Children and the University of Toronto. This work is supported by CIHR Doctoral Research Award, GSD-152386.