dampack: an R package for decision-analytic modeling
dampack R package implements useful functions to develop and analyze decision-analytic models in R. The current functions compute cost-effectiveness acceptability curves (CEAC) and frontier (CEAF), expected value of perfect information (EVPI), expected value of partial perfect information (EVPPI), sensitivity analysis (SA) using linear regression metamodeling including one- and two-way.
The package also includes functions to simulate state-transition models and produce expected outcomes of interested.
In addition, this package includes useful functions to obtain parameters of commonly used distributions
To get the current development version from github:
# install.packages("devtools") devtools::install_github("feralaes/dampack")
Documentation is still under development but the most current description of the functions in this package appears in
vignettes. Specifically, in the vignette
dampack_vignette, we provide examples on how to use the different functions of the package and in the
Markov_CEA_example vignette, we provide an example on how to run Markov models for cost-effectiveness analysis (CEA) in R using the functions of the dampack package.
Below, we provide a brief example on how to plot the cost-effectiveness acceptability curves (CEAC) and frontier (CEAF) of a three-strategy CEA using a probabilistic sensitivity analysis (PSA) dataset.
library(dampack) # Load PSA dataset data(psa) # Name of strategies strategies <- c("Chemo", "Radio", "Surgery") # Vector of WTP thresholds v.wtp <- seq(1000, 150000, by = 10000) # Matrix of costs m.c <- psa[, c(2, 4, 6)] # Matrix of effectiveness m.e <- psa[, c(3, 5, 7)] # Compute CEAF out <- ceaf(v.wtp = v.wtp, strategies = strategies, m.e = m.e , m.c = m.c, ceaf.out = TRUE) # Plot CEAF out$gg.ceaf