Matching-adjusted indirect comparison (MAIC) is popular for comparative effectiveness researches (CERs) in the absence of head-to-head trials to inform patients, physicians, and payers. It utilizes the individual patient data (IPD) of one treatment to compare with the aggregated data (AD) of another, which bridges the gap between propensity score weighting and Bucher's method. Although MAIC has been applied successfully in many CERs, through simulation studies for its statistical performance are sparse. In this simulation study, we compare MAIC with simulated treatment comparison (STC), another indirect comparison method incorporating IPD, propensity score weighting, and Bucher's method in estimating average treatment effect. Various scenarios are considered to investigate the impact of differences in baseline characteristics, type of endpoint, and deviation from the underlying assumptions.