<p>We introduce an online mathematical framework for survival analysis, allowing real time adaptation to dynamic environments and censored data. This framework enables the estimation of event time distributions through an optimal second order online convex optimization algorithm-Online Newton Step (ONS). This approach, previously unexplored, presents substantial advantages, including explicit algorithms with non-asymptotic convergence guarantees. Moreover, we analyze the selection of ONS hyperparameters, which depends on the exp-concavity property and has a significant influence on the regret bound. We introduce an adaptive aggregation method that ensures robustness in hyperparameter selection while maintaining fast regret bounds. These findings can extend beyond the survival analysis field, and are relevant for any case characterized by poor exp-concavity and unstable ONS. Additionally, we propose a stochastic approach for ONS that guarantees logarithmic regret in the case of an exponential hazard model. Next, these assertions are illustrated by simulation experiments, followed by an application to a real dataset.</p>