Censored survival data are common in clinical trials, but small control groups can pose challenges, particularly in rare diseases or where balanced randomization is impractical. Recent approaches leverage external controls from historical studies or real-world data to strengthen treatment evaluation for survival outcomes. However, using external controls directly may introduce biases due to data heterogeneity. We propose a doubly protected estimator for the treatment-specific restricted mean survival time difference that is more efficient than trial-only estimators and mitigates biases from external data. Our method adjusts for covariate shifts via doubly robust estimation and addresses outcome drift using the DR-Learner for selective borrowing. The approach can incorporate machine learning to approximate survival curves and detect outcome drifts without strict parametric assumptions, borrowing only comparable external controls. Extensive simulation studies and a real-data application evaluating the efficacy of Galcanezumab in mitigating migraine headaches have been conducted to illustrate the effectiveness of our proposed framework.

Clinical trials can take a long time and often don’t include enough patients to show if a drug helps people live longer, especially for rare diseases. To solve this problem, we developed a data-adaptive integrative approach that gathers information from previous studies or real-world data to strengthen the placebo (or control) groups. This means more patients can be assigned to the new drug, making it easier and quicker to see if the treatment is effective, and ultimately speeding up the process of getting important new medicines to people who need them.