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
formulaand specialised commands, i.e.
fit.models, which allow the user to select the inferential engine required (
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!
survHEfunctions and how they interact
A full documentation (published in the Journal of Statistical Software) is available here.
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
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:
pkgs <- c("flexsurv","Rcpp","rms","xlsx","rstan","INLA","Rtools","devtools","dplyr","ggplot2") 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
Under Linux or MacOS, it is sufficient to install the package via
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
ref="devel" instructs R to look for the relevant files in the branch named
Installation of the development version via
devtools:install_github() can fail in a
MS Windows environment with the following error message:
Error in .shlib_internal(args) : C++14 standard requested but CXX14 is not defined
This is due to known issues (see for example here) with new(er) versions of
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
dotR <- file.path(Sys.getenv("HOME"), ".R") if (!file.exists(dotR)) dir.create(dotR) M <- file.path(dotR, "Makevars.win") 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)
Baio, G. 2020. “survHE: Survival analysis for health economic evaluation and cost-effectiveness modelling.” Journal of Statistical Software 95 (14): 1–47. https://doi.org/10.18637/jss.v095.i14.