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Dynamic survival models are a flexible tool for overcoming limitations ofpopular methods in the field of survival analysis. While this flexibilityallows them to uncover more intricate relationships between covariates and thetime-to-event, it also has them running the risk of overfitting. This paperproposes a solution to this issue based on state of the art global-localshrinkage priors and shows that they are able to effectively regularize theamount of time-variation observed in the parameters. Further, a novel approachto accounting for unobserved heterogeneity in the data through a dynamic factormodel is introduced. An efficient MCMC sampler is developed and made availablein an accompanying R package. Finally, the method is applied to a current dataset of survival times of patients with adenocarcinoma of the gastroesophagealjunction.

A Bayesian Survival Model for Time-Varying Coefficients and Unobserved Heterogeneity