Bayesian prediction for flowgraph models with covariates. An application to bladder carcinoma

Statistical Flowgraph Models are an efficient tool to model multi-state stochastic processes. They support both frequentist and Bayesian approaches. Inclusion of covariates is also available. In this paper we propose an easy way to perform a Bayesian approach with covariates. Results are presented with an application to bladder carcinoma data.