## 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.

`survHE`

can take care of the following modelling aspects:

- Reconstruct individual level dataset from digitised data (e.g. from Kaplan-Meier curves)
- Analyse datasets using a hybrid of R
`formula`

and specialised commands, i.e.`fit.models`

, which allow the user to select the inferential engine required (`mle`

,`inla`

or`hmc`

), for a range of parametric models (as suggested e.g. by NICE guidelines) - Perform Probabilistic Sensitivity Analysis directly on the computed parametric survival curves
- Export the output of the statistical model to e.g. a spreadsheet, to complete the economic evaluation (e.g. using Markov models) — of course this step is
**not**necessary and the whole analysis can be embedded in a much bigger (Bayesian) model and performed directly in R!

A full documentation is available here.

## 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://www.math.ntnu.no/inla/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")
```