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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 riskdiscrimination in an internal small-sized non-EUR cohort. However, given thatexternal EUR-based model and internal individual-level data come from differentpopulations, ignoring population heterogeneity can introduce substantial bias.In this paper, we develop a Kullback-Leibler-based Cox model (CoxKL) tointegrate internal individual-level time-to-event data with external riskscores derived from published prediction models, accounting for populationheterogeneity. Partial-likelihood-based KL information is utilized to measurethe discrepancy between the external risk information and the internal data. Weestablish the asymptotic properties of the CoxKL estimator. Simulation studiesshow that the integration model by the proposed CoxKL method achieves improvedestimation efficiency and prediction accuracy. We applied the proposed methodto develop a trans-ancestry PHS model for prostate cancer and found thatintegrating a previously published EUR-based PHS with an internal genotype dataof African ancestry (AFR) males yielded considerable improvement on theprostate cancer risk discrimination.

Incorporating External Risk Information with the Cox Model under Population Heterogeneity: Applications to Trans-Ancestry Polygenic Hazard Scores