Accommodating time-varying heterogeneity in risk estimation under the Cox model: a transfer learning approach.

ABSTRACT: Transfer learning has attracted increasing attention in recent years for adaptively borrowing information across different data cohorts in various settings. Cancer registries have been widely used in clinical research because of their easy accessibility and large sample size. Our method is motivated by the question of how to utilize cancer registry data as a complement to improve the estimation precision of individual risks of death for inflammatory breast cancer (IBC) patients at The University of Texas MD Anderson Cancer Center. When transferring information for risk estimation based on the cancer registries (i.e., source cohort) to a single cancer center (i.e., target cohort), time-varying population heterogeneity needs to be appropriately acknowledged. However, there is no literature o

SUBMITTER: Li Z 

PROVIDER: S-EPMC10950074 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature