bcea {BCEA}  R Documentation 
Costeffectiveness analysis based on the results of a simulation model for a variable of clinical benefits (e) and of costs (c). Produces results to be postprocessed to give the health economic analysis. The output is stored in an object of the class "bcea".
bcea( eff, cost, ref = 1, interventions = NULL, .comparison = NULL, Kmax = 50000, wtp = NULL, plot = FALSE ) ## Default S3 method: bcea( eff, cost, ref = NULL, interventions = NULL, .comparison = NULL, Kmax = 50000, wtp = NULL, plot = FALSE ) ## S3 method for class 'rjags' bcea(eff, ...) ## S3 method for class 'rstan' bcea(eff, ...) ## S3 method for class 'bugs' bcea(eff, ...)
eff 
An object containing 
cost 
An object containing 
ref 
Defines which intervention (columns of 
interventions 
Defines the labels to be associated with each
intervention. By default and if 
.comparison 
Selects the comparator, in case of more than two
interventions being analysed. Default as NULL plots all the comparisons
together. Any subset of the possible comparisons can be selected (e.g.,

Kmax 
Maximum value of the willingness to pay to be considered.
Default value is 
wtp 
A(n optional) vector including the values of the willingness
to pay grid. If not specified then BCEA will construct a grid of 501 values
from 0 to 
plot 
A logical value indicating whether the function should produce the summary plot or not. 
... 
Additional arguments 
An object of the class "bcea" containing the following elements
n_sim 
Number of simulations produced by the Bayesian model 
n.comparators 
Number of interventions being analysed 
n.comparisons 
Number of possible pairwise comparisons 
delta.e 
For each possible comparison, the differential in the effectiveness measure 
delta.c 
For each possible comparison, the differential in the cost measure 
ICER 
The value of the Incremental CostEffectiveness Ratio 
Kmax 
The maximum value assumed for the willingness to pay threshold 
k 
The vector of values for the grid approximation of the willingness to pay 
ceac 
The value for the CostEffectiveness Acceptability Curve, as a function of the willingness to pay 
ib 
The distribution of the Incremental Benefit, for a given willingness to pay 
eib 
The value for the Expected Incremental Benefit, as a function of the willingness to pay 
kstar 
The grid approximation of the breakeven point(s) 
best 
A vector containing the numeric label of the intervention that is the most costeffective for each value of the willingness to pay in the selected grid approximation 
U 
An array including the value of the expected utility for each simulation from the Bayesian model, for each value of the grid approximation of the willingness to pay and for each intervention being considered 
vi 
An array including the value of information for each simulation from the Bayesian model and for each value of the grid approximation of the willingness to pay 
Ustar 
An array including the maximum "knowndistribution" utility for each simulation from the Bayesian model and for each value of the grid approximation of the willingness to pay 
ol 
An array including the opportunity loss for each simulation from the Bayesian model and for each value of the grid approximation of the willingness to pay 
evi 
The vector of values for the Expected Value of Information, as a function of the willingness to pay 
interventions 
A vector of labels for all the interventions considered 
ref 
The numeric index associated with the intervention used as reference in the analysis 
comp 
The numeric index(es) associated with the intervention(s) used as comparator(s) in the analysis 
step 
The step size used to form the grid approximation to the willingness to pay 
e 
The 
c 
The 
Gianluca Baio, Andrea Berardi, Nathan Green
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=TRUE # plots the results ) # Creates a summary table summary( m, # uses the results of the economic evaluation # (a "bcea" object) wtp=25000 # selects the particular value for k ) # Plots the costeffectiveness plane using base graphics ceplane.plot( m, # plots the CostEffectiveness plane comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness to pay # (default: 25,000) graph="base" # selects base graphics (default) ) # Plots the costeffectiveness plane using ggplot2 if (requireNamespace("ggplot2")) { ceplane.plot( m, # plots the CostEffectiveness plane comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness to pay # (default: 25,000) graph="ggplot2"# selects ggplot2 as the graphical engine ) # Some more options ceplane.plot( m, graph="ggplot2", pos="top", size=5, ICER_size=1.5, label.pos=FALSE, opt.theme=ggplot2::theme(text=ggplot2::element_text(size=8)) ) } # Plots the contour and scatterplot of the bivariate # distribution of (Delta_e,Delta_c) contour( m, # uses the results of the economic evaluation # (a "bcea" object) comparison=1, # if more than 2 interventions, selects the # pairwise comparison nlevels=4, # selects the number of levels to be # plotted (default=4) levels=NULL, # specifies the actual levels to be plotted # (default=NULL, so that R will decide) scale=0.5, # scales the bandwidths for both x and # yaxis (default=0.5) graph="base" # uses base graphics to produce the plot ) # Plots the contour and scatterplot of the bivariate # distribution of (Delta_e,Delta_c) contour2( m, # uses the results of the economic evaluation # (a "bcea" object) wtp=25000, # selects the willingnesstopay threshold xlim=NULL, # assumes default values ylim=NULL # assumes default values ) # Using ggplot2 if (requireNamespace("ggplot2")) { contour2( m, # uses the results of the economic evaluation # (a "bcea" object) graph="ggplot2",# selects the graphical engine wtp=25000, # selects the willingnesstopay threshold xlim=NULL, # assumes default values ylim=NULL, # assumes default values label.pos=FALSE # alternative position for the wtp label ) } # Plots the Expected Incremental Benefit for the "bcea" object m eib.plot(m) # Plots the distribution of the Incremental Benefit ib.plot( m, # uses the results of the economic evaluation # (a "bcea" object) comparison=1, # if more than 2 interventions, selects the # pairwise comparison wtp=25000, # selects the relevant willingness # to pay (default: 25,000) graph="base" # uses base graphics ) # Produces a plot of the CEAC against a grid of values for the # willingness to pay threshold ceac.plot(m) # Plots the Expected Value of Information for the "bcea" object m evi.plot(m)