  Existing literature for prediction of time-to-event data has primarily
focused on risk factors from an individual 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. Simulations and real data analysis
are conducted to show the advantage of the proposed methods compared with those
solely based on local dataset or prior models.
