ceac: Cost-Effectiveness Acceptability Curve (CEAC)
In DARTH-git/dampack: Decision-Analytic Modeling Package

Description Usage Arguments Details Value See Also Examples

ceac is used to compute and plot the cost-effectiveness acceptability curves (CEAC) from a probabilistic sensitivity analysis (PSA) dataset.

1	ceac(wtp, psa)

`wtp`	numeric vector with willingness-to-pay (WTP) thresholds
`psa`	psa object from `make_psa_obj`

ceac computes the probability of each of the strategies being cost-effective at each wtp threshold. The returned object has classes ceac and data.frame, and has its own plot method (plot.ceac).

An object of class ceac that can be visualized with plot. The ceac object is a data.frame that shows the proportion of PSA samples for which each strategy at each WTP threshold is cost-effective. The final column indicates whether or not the strategy at a particular WTP is on the cost-efficient frontier.

plot.ceac, summary.ceac

# psa input provided with package
data("example_psa")
example_psa_obj <- make_psa_obj(example_psa$cost, example_psa$effectiveness,
                    example_psa$parameters, example_psa$strategies)

# define wtp threshold vector (can also use a single wtp)
wtp <- seq(1e4, 1e5, by = 1e4)
ceac_obj <- ceac(wtp, example_psa_obj)
plot(ceac_obj) # see ?plot.ceac for options

# this is most useful when there are many strategies
# warnings are printed to describe strategies that
# have been filtered out
plot(ceac_obj, min_prob = 0.5)

# standard ggplot layers can be used
plot(ceac_obj) +
    labs(title = "CEAC", y = "Pr(Cost-effective) at WTP")

# the ceac object is also a data frame
head(ceac_obj)

# summary() tells us the regions of cost-effectiveness for each strategy.
# Note that the range_max column is an open parenthesis, meaning that the
# interval over which that strategy is cost-effective goes up to but does not include
# the value in the range_max column.
summary(ceac_obj)