Kullback-Leibler-Based Discrete Failure Time Models for Integration of Published Prediction Models with New Time-To-Event Dataset

TL;DR

This paper introduces a novel discrete failure time modeling approach using Kullback-Leibler divergence to effectively integrate external published prediction models with internal time-to-event data, addressing heterogeneity and privacy issues.

Contribution

It proposes a new method that accounts for heterogeneity by measuring distribution discrepancies, improving prognosis prediction by combining external models with internal data.

Findings

Simulation studies demonstrate the method's superiority over existing approaches.

Application to kidney transplant data shows improved prediction accuracy.

The approach effectively handles heterogeneity and privacy constraints.

Abstract

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 large-scale studies is expected to improve the performance of prognosis prediction on internal individual-level time-to-event data. However, existing integration approaches typically assume that underlying distributions from 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 discrete failure time modeling procedure, which utilizes a discrete hazard-based Kullback-Leibler discriminatory information measuring the discrepancy between the published models and the internal dataset. Simulations show the advantage of the proposed method compared with those solely based on the internal data or published models. We apply the proposed method to improve prediction performance on a kidney transplant dataset from a local hospital by integrating this small-scale dataset with published survival models obtained from the national transplant registry.