  Polygenic hazard score (PHS) models designed for European ancestry (EUR)
individuals provide ample information regarding survival risk discrimination.
Incorporating such information can improve the performance of risk
discrimination in an internal small-sized non-EUR cohort. However, given that
external EUR-based model and internal individual-level data come from different
populations, ignoring population heterogeneity can introduce substantial bias.
In this paper, we develop a Kullback-Leibler-based Cox model (CoxKL) to
integrate internal individual-level time-to-event data with external risk
scores derived from published prediction models, accounting for population
heterogeneity. Partial-likelihood-based KL information is utilized to measure
the discrepancy between the external risk information and the internal data. We
establish the asymptotic properties of the CoxKL estimator. Simulation studies
show that the integration model by the proposed CoxKL method achieves improved
estimation efficiency and prediction accuracy. We applied the proposed method
to develop a trans-ancestry PHS model for prostate cancer and found that
integrating a previously published EUR-based PHS with an internal genotype data
of African ancestry (AFR) males yielded considerable improvement on the
prostate cancer risk discrimination.
