survHE: Survival analysis in health economic evaluation
Survival analysis in health economic evaluation
Contains a suite of functions to streamline systematically 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 (HMC; 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
orhmc
), 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!
survHE
functions and how they interact
A full documentation (published in the Journal of Statistical Software) is available here.
Installation
There are two ways of installing survHE
. A “stable” version (as of 7 October 2020, it is on version 1.1.1
) is packaged and binary files are available for Windows and as source. To install the stable version from CRAN, run the following commands
install.packages("survHE")
This process can be quite lengthy, if you miss many of the relevant packages. Also, the pre-compiled rstan
models do take some time at installation (but this steps produces substantial savings at compilation and running time!).
survHE
is also available from the GitHub repository. The master
branch is the same as the official one, hosted on CRAN. You can still install it from GitHub using the following commands on the R terminal. On a Windows machine:
<- c("flexsurv","Rcpp","rms","xlsx","rstan","INLA","Rtools","devtools","dplyr","ggplot2")
pkgs <- c("https://cran.rstudio.com", "https://inla.r-inla-download.org/R/stable")
repos install.packages(pkgs,repos=repos,dependencies = "Depends")
before installing the package using devtools
:
::install_github("giabaio/survHE") devtools
Under Linux or MacOS, it is sufficient to install the package via devtools
:
install.packages("devtools")
:install_github("giabaio/survHE") devtools
Finally, there is a development version, which is stored in the devel
branch of the GitHub repository. This version is continuously updated (and we welcome comments and suggestions - you can open an “Issue” here). The process for installation is essentially the same as above with the only final difference
:install_github("giabaio/survHE", ref="devel") devtools
(the option ref="devel"
instructs R to look for the relevant files in the branch named devel
).
We recommend using the GitHub installation from the master repository as it will fix minor issues that may become known quicker than the stable version on CRAN.
Installation issues
Installation of the development version via devtools:install_github()
can fail in a MS Windows
environment with the following error message:
in .shlib_internal(args) : C++14 standard requested but CXX14 is not defined Error
This is due to known issues (see for example here) with new(er) versions of rstan
(which survHE
uses for full Bayesian modelling). rstan
uses by default version 14 of the C++
compiler, so R
needs to know and act accordingly. This can be solved by running the following code
<- file.path(Sys.getenv("HOME"), ".R")
dotR if (!file.exists(dotR))
dir.create(dotR)
<- file.path(dotR, "Makevars.win")
M if (!file.exists(M))
file.create(M)
cat("\nCXX14FLAGS=-O3 -Wno-unused-variable -Wno-unused-function",
"CXX14 = $(BINPREF)g++ -m$(WIN) -std=c++1y",
"CXX11FLAGS=-O3 -Wno-unused-variable -Wno-unused-function",
file = M, sep = "\n", append = TRUE)
Last updated: 11 December 2022