R package with useful functions to develop and analyze decision-analytic models
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feralaes MAJOR UPDATE evppi_lrmm.R
Added option to fit polynomial models (including a linear model) and teh possibility to selecet the number of basis functions of the splnes or degree of polynomial, k.
Latest commit 41cee2b May 1, 2018


dampack: an R package for decision-analytic modeling

The 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")


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.

# Load PSA dataset
# 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

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