Leveraging External Individualized Prediction Models in Bayesian Survival Analysis

Speaker: Mi-Ok Kim, Phd, MS, MA, Professor of Biostatistics, UCSF

Individualized risk prediction algorithms, such as the Prostate Cancer Risk Assessment tool, are increasingly used to predict cancer relapse or progression. Since these algorithms are typically trained on large datasets, effectively integrating their outputs can enhance the efficiency of analyzing individual studies. In this research, we consider the Bayesian approach to Cox regression analysis for right censored time-to-event outcomes and the incorporation of external information provided by large-scale prediction models.

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