Contains a suite of functions to systematise the workflow involving survival analysis in health economic evaluation. survHE can fit a large range of survival models using both a frequentist approach (by calling the R package flexsurv) and a Bayesian perspective. For a selected range of models, both Integrated Nested Laplace Integration (via the R package INLA) and Hamiltonian Monte Carlo (via the R package rstan) are possible. HMC models are pre-compiled so that they can run in a very efficient and fast way. In addition to model fitting, survHE provides a set of specialised functions, for example to perform Probabilistic Sensitivity Analysis, export the results of the modelling to a spreadsheet, plotting survival curves and uncertainty around the mean estimates.
NB: To run the Bayesian models, as of version 2.0 of survHE, it is necessary to install the additional packages survHEinla and/or survHEhmc, which are available from this GitHub repository. The reason for this structural change is that in this way, the basic backbone of survHE (available from this main branch of the repo) becomes a very lean package, whose installation is very quick. More details here. All the functionalities are in place for survHE to easily extend to the Bayesian versions, once one or both of the additional "modules" is also installed.
The most updated version can be installed using the following code.
install.packages("remotes")
remotes::install_github("giabaio/survHE")To run the Bayesian versions of the models, you also need to install the ancillary packages
# Bayesian models using HMC/Stan
remotes::install_github("giabaio/survHEhmc")
# or alternatively
install.packages("survHEhmc",repos=c("https://giabaio.github.io/drat/","https://www.stats.bris.ac.uk/R/"),type="source")
# Bayesian models using INLA
remotes::install_github("giabaio/survHEinla")
# or alternatively
install.packages("survHEinla",repos=c("https://giabaio.github.io/drat/","https://www.stats.bris.ac.uk/R/"),type="source")(these two are optional, in some sense, so you don't have to, unless you want to do the right thing and be Bayesian about it... 😉)