KULLBACK-LEIBLER-BASED DISCRETE FAILURE TIME MODELS FOR INTEGRATION OF PUBLISHED PREDICTION MODELS WITH NEW TIME-TO-EVENT DATASET.

Prediction of time-to-event data often suffers from rare event rates, small sample sizes, high dimensionality, and low signal-to-noise ratios. Incorporating published prediction models from external large-scale studies is expected to improve the performance of prognosis prediction from internal individual-level data. However, existing integration approaches typically assume that the underlying distributions of the external and internal data sources are similar, which is often invalid. To account for challenges, including heterogeneity, data sharing, and privacy constraints, we propose a failure time integration procedure, which utilizes a discrete hazard-based Kullback-Leibler discriminatory information measuring the discrepancy between the external models and the internal dataset. The asymptotic properties and simulation results show the advantage of the proposed method compared to those solely based on internal data. We apply the proposed method to improve prediction performance on a kidney transplant dataset from a local hospital by integrating this small-sized dataset with a published survival model obtained from the national transplant registry.