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

README.md

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

Installation

To get the current development version from github:

# install.packages("devtools")
devtools::install_github("feralaes/dampack")

Documentation

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.

Example

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

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