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)