Kullback-Leibler-Based Discrete Relative Risk Models for Integration of Published Prediction Models with New Dataset
Proceedings of AAAI Spring Symposium on Survival Prediction - Algorithms, Challenges, and Applications 2021, PMLR 146:232-239, 2021.
Existing literature for prediction of time-to-event data has primarily focused on risk factors from a single individual-level dataset. However, these analyses may suffer from small sample sizes, high dimensionality and low signal-to-noise ratios. To improve prediction stability and better understand risk factors associated with outcomes of interest, we propose a Kullback-Leibler-based discrete relative risk modeling procedure to borrow information from existing models. Simulations and real data analysis were conducted to show the advantage of the proposed method compared with those solely based on data from current study or prior information.