Efficient Risk Assessment of Time-to-Event Targets With Adaptive Information Transfer.

Using informative sources to enhance statistical analysis in target studies has become an increasingly popular research topic. However, cohorts with time-to-event outcomes have not received sufficient attention, and external studies often encounter issues of incomparability due to population heterogeneity and unmeasured risk factors. To improve individualized risk assessments, we propose a novel methodology that adaptively borrows information from multiple incomparable sources. By extracting aggregate statistics through transitional models applied to both the external sources and the target population, we incorporate this information efficiently using the control variate technique. This approach eliminates the need to load individual-level records from sources directly, resulting in low computational complexity and strong privacy protection. Asymptotically, our estimators of both relative and baseline risks are more efficient than traditional results, and the power of covariate effects testing is much enhanced. We demonstrate the practical performance of our method via extensive simulations and a real case study.