plot.CEriskav {BCEA} | R Documentation |
Summary plot of the health economic analysis when risk aversion is included.
## S3 method for class 'CEriskav' plot(x, pos = c(0, 1), graph = c("base", "ggplot2"), ...)
x |
An object of the class |
pos |
Parameter to set the position of the legend (only relevant for
multiple interventions, ie more than 2 interventions being compared).
Can be given in form
of a string |
graph |
A string used to select the graphical engine to use for
plotting. Should (partial-)match the two options |
... |
Arguments to be passed to methods, such as graphical parameters
(see |
Plots the Expected Incremental Benefit and the Expected Value of Perfect Information when risk aversion is included in the utility function.
list(eib,evi) |
A two-elements named list of the ggplot objects
containing the requested plots. Returned only if |
The function produces two plots for the risk aversion analysis. The first
one is the EIB as a function of the discrete grid approximation of the
willingness parameter for each of the possible values of the risk aversion
parameter, r
. The second one is a similar plot for the EVPI.
Gianluca Baio, Andrea Berardi
Baio, G., Dawid, A. P. (2011). Probabilistic Sensitivity Analysis in Health Economics. Statistical Methods in Medical Research doi:10.1177/0962280211419832.
Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London
# See Baio G., Dawid A.P. (2011) for a detailed description of the # Bayesian model and economic problem # # Load the processed results of the MCMC simulation model data(Vaccine) # # Runs the health economic evaluation using BCEA m <- bcea(e=e,c=c, # defines the variables of # effectiveness and cost ref=2, # selects the 2nd row of (e,c) # as containing the reference intervention interventions=treats, # defines the labels to be associated # with each intervention Kmax=50000, # maximum value possible for the willingness # to pay threshold; implies that k is chosen # in a grid from the interval (0,Kmax) plot=FALSE # inhibits graphical output ) # # Define the vector of values for the risk aversion parameter, r, eg: r <- c(1e-10, 0.005, 0.020, 0.035) # # Run the cost-effectiveness analysis accounting for risk aversion CEriskav(m) <- r # # produce the plots plot(m) ## Alternative options, using ggplot2 plot(m, graph = "ggplot2")