Most existing risk prediction tools for time-to-event outcomes were developed using large epidemiologic cohorts. Although these data sources are often of high quality, their external generalizability to a much broader population, termed target population in this paper, may be limited. Many external validation studies of existing risk prediction tools have shown adequate discrimination but poor calibration which is largely driven by the differences in the baseline hazard between the sourcing cohort and the target population. In this paper, we developed a statistical method to reconcile this discrepancy by incorporating the composite incidence of the target population in the development of risk prediction tools using the sourcing cohort. An individualized absolute risk prediction tool could then be improved using the proposed baseline hazard estimator. The asymptotic properties of the proposed absolute risk estimators were established. Simulation studies examining small sample properties of the proposed estimators were conducted. The proposed methods will be illustrated using the Women’s Health Initiative Observational Study and Canary Prostate Active Surveillance Study.