WITHDRAWN Relative Entropy-Based Discrete Relative Risk Models for Integrated Prediction of Competing Risk

The contemporary data landscape is enriched with an abundance of biobank data providing an unprecedented array of wealthier risk factors with more detailed competing risk outcomes fueling efforts to enhance prognostic predictions. However, the newly obtained data suffer from rare event rates, limited sample sizes, and high dimensionality. The presence of competing risks exacerbates these limitations, reducing the stability of estimation and prediction. To address these challenges, the incorporation of historical prediction models has been recognized as a promising strategy. However, prevailing integration methods often hinge on the strong assumptions of uniform survival outcome type, effect size and covariate space across disparate data sources - assumptions that frequently diverge from reality. In response, we propose a longitudinal multinomial relative entropy-based integration framework, which effectively incorporates summary-level data from established prediction models and account for patient privacy and data sharing constraints. We apply this innovative integration methodology to enhance the prediction of kidney transplant outcomes in patients during the COVID-19 pandemic.

Competing risk

Survival analysis

Data integration

Kidney transplantation

COVID-19