sim_table {BCEA}

Table of Simulation Statistics for the Health Economic Model

Description

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.

Usage

sim_table(he, ...)

## S3 method for class 'bcea'
sim_table(he, wtp = 25000, ...)

Arguments

he

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

...

Additional arguments

wtp

The value of the willingness to pay threshold to be used in the summary table.

Value

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

Author(s)

Gianluca Baio

References

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

bcea()

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,                # defines the variables of 
          c=cost,               # 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 
          wtp=25000) # selects the particular value for k