Laplacian-P-splines for Bayesian inference in the mixture cure model.

ABSTRACT: The mixture cure model for analyzing survival data is characterized by the assumption that the population under study is divided into a group of subjects who will experience the event of interest over some finite time horizon and another group of cured subjects who will never experience the event irrespective of the duration of follow-up. When using the Bayesian paradigm for inference in survival models with a cure fraction, it is common practice to rely on Markov chain Monte Carlo (MCMC) methods to sample from posterior distributions. Although computationally feasible, the iterative nature of MCMC often implies long sampling times to explore the target space with chains that may suffer from slow convergence and poor mixing. Furthermore, extra efforts have to be invested in diagnostic chec

SUBMITTER: Gressani O 

PROVIDER: S-EPMC9542184 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature