  Survival data is encountered in a range of disciplines, most notably health
and medical research. Although Bayesian approaches to the analysis of survival
data can provide a number of benefits, they are less widely used than classical
(e.g. likelihood-based) approaches. This may be in part due to a relative
absence of user-friendly implementations of Bayesian survival models. In this
article we describe how the rstanarm R package can be used to fit a wide range
of Bayesian survival models. The rstanarm package facilitates Bayesian
regression modelling by providing a user-friendly interface (users specify
their model using customary R formula syntax and data frames) and using the
Stan software (a C++ library for Bayesian inference) for the back-end
estimation. The suite of models that can be estimated using rstanarm is broad
and includes generalised linear models (GLMs), generalised linear mixed models
(GLMMs), generalised additive models (GAMs) and more. In this article we focus
only on the survival modelling functionality. This includes standard parametric
(exponential, Weibull, Gompertz) and flexible parametric (spline-based) hazard
models, as well as standard parametric accelerated failure time (AFT) models.
All types of censoring (left, right, interval) are allowed, as is delayed entry
(left truncation), time-varying covariates, time-varying effects, and frailty
effects. We demonstrate the functionality through worked examples. We
anticipate these implementations will increase the uptake of Bayesian survival
analysis in applied research.
