make_psa_obj | R Documentation |
Creates an object to hold probabilistic sensitivity analysis data, while checking the data for validity. The object can then be used for many standard cost-effectiveness analyses (see Details below).
make_psa_obj(
cost,
effectiveness,
parameters = NULL,
strategies = NULL,
currency = "$",
other_outcome = NULL
)
cost |
For the data.frame, each simulation should be a row and each strategy should be a column.
Naming the columns of the data frames is not necessary, as they will be renamed with
the |
effectiveness |
For the data.frame, each simulation should be a row and each strategy should be a column.
Naming the columns of the data frames is not necessary, as they will be renamed with
the |
parameters |
Data frame with values for each simulation (rows) and parameter (columns). The column names should be the parameter names. |
strategies |
vector with the names of the strategies. Due to requirements in
certain uses of this vector, this function uses |
currency |
symbol for the currency being used (ex. "$", "£") |
other_outcome |
data.frame containing values for another user-defined outcome.
Each simulation should be a row of the data frame, and each strategy should be a column.
Naming the columns of the data frames is not necessary, as they will be renamed with
the |
The PSA object forms the backbone of one part of the dampack
package.
A scatterplot of the cost-effectiveness plane may be shown by running plot
on the output of make_psa_obj
.
Using this object, you may calculate:
Cost-effectiveness acceptability curves (ceac
)
Expected value of perfect information (calc_evpi
)
Expected loss (calc_exp_loss
)
One-way sensitivity analysis (owsa
)
Two-way sensitivity analysis (twsa
)
Metamodels (metamodel
)
In addition, the PSA may be converted to a base-case analysis by using summary
on the PSA object. The output of summary
can be used in calculate_icers
.
An object of class psa
summary.psa
, plot.psa
# psa input provided with package
data("example_psa")
psa <- make_psa_obj(example_psa$cost, example_psa$effectiveness,
example_psa$parameters, example_psa$strategies)
# custom print and summary methods
print(psa)
summary(psa)
# custom plot method; see ?plot.psa for options
plot(psa)
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