sim.table {BCEA} | R Documentation |
Using the input in the form of MCMC simulations and after having run the health economic model, produces a summary table of the simulations from the cost-effectiveness analysis
sim.table(he, wtp = 25000)
he |
A |
wtp |
The value of the willingness to pay threshold to be used in the summary table. |
Produces the following elements:
Table |
A table with the simulations from the economic model |
names.cols |
A vector of labels to be associated with each column of the table |
wtp |
The selected value of the willingness to pay |
ind.table |
The index associated with the selected value of the willingness to pay threshold in the grid used to run the analysis |
Gianluca Baio
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) ) # # Now can save the simulation exercise in an object using sim.table() st <- sim.table(m, # uses the results of the economic evalaution # (a "bcea" object) wtp=25000 # selects the particular value for k ) # # The table can be explored. For example, checking the # element 'Table' of the object 'st'