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.

翻译：用于预测时间到活动数据的现有文献主要侧重于单个数据集的风险因素,但这些分析可能因样本规模小、高度和信号到噪音比率低而受到影响。为了提高预测稳定性和更好地了解与相关结果相关的风险因素,我们提议采用基于库尔回背-利贝尔的离散相对风险模型程序。进行模拟和真实数据分析是为了显示拟议方法与完全基于当地数据集或先前模型的方法相比的优势。