| mixedAn<- {BCEA} | R Documentation | 
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) <- value
Arguments
he | 
 A   | 
value | 
 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.  | 
Value
Creates an object in the class mixedAn, a subclass of bcea
which contains the results of the health economic evaluation in the mixed analysis case:
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  | 
mkt.shares | 
 The vector of market shares associated with each available intervention  | 
Author(s)
Gianluca Baio
References
Baio G, Russo P (2009). “A decision-theoretic framework for the application of cost-effectiveness analysis in regulatory processes.” Pharmacoeconomics, 27(8), 5–16. ISSN 20356137, doi:10.1007/bf03320526.
Baio G, Dawid aP (2011). “Probabilistic sensitivity analysis in health economics.” Stat. Methods Med. Res., 1–20. ISSN 1477-0334, doi:10.1177/0962280211419832, https://pubmed.ncbi.nlm.nih.gov/21930515/.
Baio G (2013). Bayesian Methods in Health Economics. CRC.
See Also
Examples
# 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=eff, c=cost,    # 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
mixedAn(m) <- NULL      # uses the results of the mixed strategy 
                        #  analysis (a "mixedAn" object)
                        # the vector of market shares can be defined 
                        #  externally. If NULL, then each of the T 
                        #  interventions will have 1/T market share
                        # produces the plots
evi.plot(m)