In real-world longitudinal studies, time-varying confounding often exists. It can lead to a severely biased estimate of treatment effect if not accounted for appropriately. A popular way of adjusting for time-varying confounding is through the marginal structural model (MSM). For the estimation of MSM parameters, doubly robust (DR) methods are more desirable due to their robustness against model mis-specification of either the propensity score model or the outcome regression model. Two commonly used DR estimation methods are augmented inverse probability weighting (AIPW) and targeted maximum likelihood estimation (TMLE). Although both have the DR property, their performance may not always be the same. In this research, we will conduct a simulation study to compare the performance of AIPW and TMLE. In the simulation, longitudinal data under dynamic treatment regime will be simulated and machine learning method will be employed to model both the propensity score and the outcome. The estimates from AIPW and TMLE will be compared based on their bias and mean squared error. Special attention will be paid to the impact of sample size and number of confounding variables in the model.