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, ...) ## S3 method for class 'bcea' sim_table(he, wtp = 25000, ...)
he |
A |
... |
Additional arguments |
wtp |
The value of the willingness to pay threshold to be used in the summary table. |
Produces the following elements:
table |
A table with simulation statistics 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 |
idx_wtp |
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, # defines the variables of c=c, # 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() sim_table(m, # uses the results of the economic evaluation # (a 'bcea' object) wtp=25000) # selects the particular value for k