Survival Analysis in Health Economic Evaluation

Contains a suite of functions for survival analysis in health economics. These can be used to run survival models under a frequentist (based on maximum likelihood) or a Bayesian approach (both based on Integrated Nested Laplace Approximation or Hamiltonian Monte Carlo). The user can specify a set of parametric models using a common notation and select the preferred mode of inference. The results can also be post-processed to produce probabilistic sensitivity analysis and can be used to export the output to an Excel file (e.g. for a Markov model, as often done by modellers and practitioners).

Survival analysis in health economic evaluation

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.

Installation

There are two ways of installing survHE. A "stable" version is packaged and binary files are available for Windows and as source. To install the stable version on a Windows machine, run the following commands

install.packages("survHE",
    repos=c("http://www.statistica.it/gianluca/R",
        "https://cran.rstudio.org",
                "https://inla.r-inla-download.org/R/stable"),
    dependencies=TRUE
)

Note that you need to specify a vector of repositories - the first one hosts survHE, while the second one should be an official CRAN mirror. You can select whichever one you like, but a CRAN mirror must be provided, so that install.packages() can also install the "dependencies" (e.g. other packages that are required for survHE to work). The third one is used to install the package INLA, which is used to do one version of the Bayesian analysis. This process can be quite lengthy, if you miss many of the relevant packages.

To install from source (e.g. on a Linux machine), run

install.packages("survHE",
    repos=c("http://www.statistica.it/gianluca/R",
        "https://cran.rstudio.org",
        "https://inla.r-inla-download.org/R/stable"),
    type="source",
    dependencies=TRUE
)

The second way involves using the "development" version of survHE - this will usually be updated more frequently and may be continuously tested. On Windows machines, you need to install a few dependencies, including Rtools first, e.g. by running

pkgs <- c("flexsurv","Rcpp","rms","xlsx","rstan","INLA","Rtools","devtools")
repos <- c("https://cran.rstudio.com", "https://inla.r-inla-download.org/R/stable") 
install.packages(pkgs,repos=repos,dependencies = "Depends")

before installing the package using devtools:

devtools::install_github("giabaio/survHE")

Under Linux or MacOS, it is sufficient to install the package via devtools:

install.packages("devtools")
devtools:install_github("giabaio/survHE")