mixedAn: Cost-effectiveness analysis when multiple (possibly non cost-effective) interventions are present on the market

Description

Runs the cost-effectiveness analysis, but accounts for the fact that more than one intervention is present on the market

Usage

mixedAn(he, mkt.shares = NULL, plot = FALSE)
"mixedAn"(he, mkt.shares = NULL, plot = FALSE)

Arguments

A bcea object containing the results of the Bayesian modelling and the economic evaluation.

mkt.shares

A vector of market shares associated with the interventions. Its size is the same as the number of possible comparators. By default, assumes uniform distribution for each intervention.

plot

Logical value indicating whether the function should produce graphical output, via plot.mixedAn, or not. Default is set to FALSE.

Value

Ubar: An array with the simulations of the ''known-distribution'' mixed utilities, for each value of the discrete grid approximation of the willingness to pay parameter
OL.star: An array with the simulations of the distribution of the Opportunity Loss for the mixed strategy, for each value of the discrete grid approximation of the willingness to pay parameter
evi.star: The Expected Value of Information for the mixed strategy, for each value of the discrete grid approximation of the willingness to pay parameter
k: The discrete grid approximation of the willingness to pay parameter used for the mixed strategy analysis
Kmax: The maximum value of the discrete grid approximation for the willingness to pay parameter
step: The step used to form the grid approximation to the willingness to pay
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
mkt.shares: The vector of market shares associated with each available intervention
n.comparisons: The total number of pairwise comparisons available
interventions: A vector of labels for all the interventions considered
evi: The vector of values for the ''optimal'' Expected Value of Information, as a function of the willingness to pay

References

Baio, G. and Russo, P. (2009).A decision-theoretic framework for the application of cost-effectiveness analysis in regulatory processes. Pharmacoeconomics 27(8), 645-655 doi:10.2165/11310250

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

Examples

Run this code

# 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
)
#
ma <- mixedAn(m,        # uses the results of the mixed strategy 
                        #  analysis (a "mixedAn" object)
      mkt.shares=NULL,  # the vector of market shares can be defined 
                        #  externally. If NULL, then each of the T 
                        #  interventions will have 1/T market share
      plot=TRUE         # produces the plots
)