Computes the weights to be associated with a set of competing models in order to perform structural PSA.
struct.psa(
models,
effect,
cost,
ref = NULL,
interventions = NULL,
Kmax = 50000,
plot = FALSE
)List object of bcea object, model weights and DIC
A list containing the output from either R2jags or R2WinBUGS for all the models that need to be combined in the model average
A list containing the measure of effectiveness computed from the various models (one matrix with n.sim x n.ints simulations for each model)
A list containing the measure of costs computed from the various models (one matrix with n.sim x n.ints simulations for each model)
Which intervention is considered to be the reference
strategy. The default value ref=1 means that the intervention
appearing first is the reference and the other(s) is(are) the comparator(s)
Defines the labels to be associated with each
intervention. By default and if NULL, assigns labels in the form
"Intervention1", ... , "InterventionT"
Maximum value of the willingness to pay to be considered.
Default value is k=50000. The willingness to pay is then approximated
on a discrete grid in the interval [0,Kmax]. The grid is equal to
wtp if the parameter is given, or composed of 501 elements if
wtp=NULL (the default)
A logical value indicating whether the function should produce the summary plot or not
Gianluca Baio
The model is a list containing the output from either R2jags or R2WinBUGS for all the models that need to be combined in the model average effect is a list containing the measure of effectiveness computed from the various models (one matrix with n_sim x n_ints simulations for each model) cost is a list containing the measure of costs computed from the various models (one matrix with n_sim x n_ints simulations for each model).
Baio G. (2012). Bayesian Methods in Health Economics. CRC/Chapman Hall, London.
bcea