Abstract: Estimating causal inferences in observational studies with time varying covariates require methods that can address complexities such as non-random allocation of patients’ to treatment groups, possible censoring of, exposure variables e.g., time to antiretroviral therapy (ART) initiation, and outcome variables e.g., mortality. Marginal Structural Cox Proportional Hazard Model (MSCM) adopts inverse probability of treatment and censoring weights to correct for non-random allocation of treatment groups, patient attrition or administrative censoring and confounding.