ABSTRACT In clinical trials with time‐to‐event data, the evaluation of treatment efficacy can be a long and complex process, especially when considering long‐term primary endpoints. Using surrogate endpoints to correlate the primary endpoint has become a common practice to accelerate decision‐making. Moreover, the ethical need to minimize sample size and the practical need to optimize available resources have encouraged the scientific community to develop methodologies that leverage historical data. Relying on the general theory of group sequential design and using a Bayesian framework, the methodology described in this paper exploits a documented historical relationship between a clinical “final” endpoint and a surrogate endpoint to build an informative prior for the primary endpoint, using surrogate data from an early interim analysis of the clinical trial. The predictive probability of success of the trial is then used to define a futility‐stopping rule. The methodology demonstrates substantial enhancements in trial operating characteristics when there is a good agreement between current and historical data. Furthermore, incorporating a robust approach that combines the surrogate prior with a vague component mitigates the impact of the minor prior‐data conflicts while maintaining acceptable performance even in the presence of significant prior‐data conflicts. The proposed methodology was applied to design a Phase III clinical trial in metastatic colorectal cancer, with overall survival as the primary endpoint and progression‐free survival as the surrogate endpoint.