Individual Patient Data Meta-Analysis (IPD-MA) aims to combine patient-level data from several similar studies to evaluate the effectiveness of treatment of interest. In IPD-MA of observational studies, patient’s profiles are used to adjust for confounding which may minimize heterogeneity across studies. There are two strategies for the analysis of IPD-MA: One-step approach and two-step approach. In both approaches adjusting for confounding may be performed by adding covariates into the regression model. To better address this problem a better alternative could be the use of propensity score matching (PSM) approaches. In this work, we conduct a simulation study to evaluate the performance of various approaches to PSM in the context of IPD-MA. We demonstrate the approaches in an IPD-MA that investigate treatment success of various drugs for multiple drug resistant tuberculosis.