Dynamic information is crucial for monitoring and predicting patients’ health status. The Time-Dependent Cox Model (TDCM) is widely used to analyze longitudinal biomarkers and time-to-event data. However, when dealing with endogenous variables, as biomarkers, the terminal event truncates the observation of their full trajectory, leading to Missing Not At Random (MNAR) data. Joint Models (JMs) accommodate this type of MNAR data by explicitly modeling the missing process due to terminating event. They integrate a longitudinal sub-model for the biomarker trajectory, through a mixed-effects framework, with an event sub-model. However, challenges remain when biomarker measurements are subject to other MNAR sources. This study uses simulations to assess JMs’ robustness under different missing data mechanisms, comparing them to TDCM. Simulation results indicated that JM remains robust despite truncated or intermittently missing markers, but the presence of elevated measurement error increases uncertainty and can cause moderate-to-large bias. The TDCM remains effective with minimal measurement error, but joint modeling is preferable when error is abundant, especially under MNAR missingness in longitudinal data, beyond just informative censoring. Nonetheless, correctly modeling of the trajectory, as seen, is essential for conducting a robust analysis. The JM is also applied in Intensive Care setting with multiple missing sources.