Survival Analysis in Health Economic Evaluation
A fictional survival trial.
Format digitised data for use in survival analysis
Fit parametric survival analysis for health economic evaluations
make_data_multi_state
Creates a 'newdata' list to modify the plots for specific individual p...
Create an individual level dataset from digitised data
Engine for Probabilistic Sensitivity Analysis on the survival curves
make.transition.probs
Markov trace
Graphical representation of the measures of model fitting based on Inf...
Plot to assess suitability of parametric model
Plot survival curves for the models fitted using fit.models
Print a summary of the survival model(s) fitted by fit.models
Graphical depiction of the probabilistic sensitivity analysis for the ...
Prints a summary table for the distribution the mean survival time for...
survHE: Survival Analysis in Health Economic Evaluation
NICE TA174 dataset.
A Custom ggplot2 Theme for Survival Plots
three_state_mm
write.surv
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). To run the Bayesian models, the user needs to install additional modules (packages), i.e. 'survHEinla' and 'survHEhmc'. These can be installed from <https://giabaio.r-universe.dev/> using 'install.packages("survHEhmc", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))' and 'install.packages("survHEinla", repos = c("https://giabaio.r-universe.dev", "https://cloud.r-project.org"))' respectively. 'survHEinla' is based on the package INLA, which is available for download at <https://inla.r-inla-download.org/R/stable/>. 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). <doi:10.18637/jss.v095.i14>.
Useful links