summary.mixedAn {BCEA} | R Documentation |
mixedAn
(mixed analysis)
Prints a summary table for the results of the mixed analysis for the economic evaluation of a given model
## S3 method for class 'mixedAn' summary(object, wtp = 25000,...)
object |
An object of the class |
wtp |
The value of the willingness to pay choosen to present the analysis. |
... |
Additional arguments affecting the summary produced. |
Produces a table with summary information on the loss in expected value of information generated by the inclusion of non cost-effective interventions in the market.
Gianluca Baio
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
# 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) ) # 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 ) # # Prints a summary of the results summary(ma, # uses the results of the mixed strategy analysis # (a "mixedAn" object) wtp=25000 # selects the relevant willingness to pay # (default: 25,000) )