Grant Number:	1R01CA316177-01 Interpret this number
Primary Investigator:	Ning, Jing
Organization:	University Of Tx Md Anderson Can Ctr
Project Title:	Advancing Transfer Learning Using Surrogates, Family History, and Prior Models.
Fiscal Year:	2026

Project Summary: Accurate clinical decision-making increasingly depends on the integration of diverse data resources, including established risk prediction models, surrogate outcomes, and family medical history. Transfer learning offers a powerful tool to adaptively leverage these heterogeneous sources, improving model performance when target data are limited or differ substantially from prior studies. However, existing transfer learning approaches often rely on restrictive assumptions, such as identical risk factors and measurement scales across studies or require complex joint modeling that is impractical in many real-world settings. In addition, family history data pose challenges due to recall bias, missingness, and sparsity, limiting their predictive utility. The objective of this proposal is to address these challenges by developing rigorous, flexible, and robust methods for knowledge transfer, tailored to the unique characteristics of each setting. Specifically, we will: 1) Develop rank-based transfer learning approaches that leverage relative risk rankings from existing models, enabling integration of new variables such as novel biomarkers while addressing population heterogeneity and preventing negative transfer; 2) Create systematic frameworks for incorporating surrogate data to improve the efficiency of primary endpoint analyses without requiring burdensome joint modeling, while adjusting for covariate and label shifts; and 3) Propose empirical likelihood approaches with novel bias- correction methods to integrate family health history data, explicitly accounting for recall bias to enhance risk assessment and prediction accuracy. These methodological innovations will be applied to motivating studies to demonstrate gains in statistical efficiency and clinical utility. To ensure broad dissemination and maximize impact, we will provide user-friendly, open-access software tools with comprehensive documentation. By bridging methodological innovation with real-world application, this project aims to significantly advance the use of transfer learning in biomedical research and support more precise, evidence-based clinical decision-making.





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