Bayesian statistics versus classical statistics in survival analysis: an applicable example.

Background: Heart disease is the leading cause of death in the world and 17 million people die from cardiovascular diseases around the world each year, so finding factors that affect the survival of these patients is of particular importance. Therefore, finding the best model to analyze patient survival can help to find more accurate results.

Methods: There are different methods to survival analysis that assess one or more risk factors; the classic Kaplan-Meier method, Cox regression, parametric survival models, and newer models such as Bayesian survival.