% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/cea.R
\name{mb_ceac}
\alias{mb_ceac}
\title{Cost-effectiveness acceptability curve}
\usage{
mb_ceac(delta_cost, delta_effect, wtp_grid)
}
\arguments{
\item{delta_cost}{Numeric vector. Sampled incremental costs of B
relative to A.}

\item{delta_effect}{Numeric vector, same length as \code{delta_cost}.
Sampled incremental effects.}

\item{wtp_grid}{Numeric vector of willingness-to-pay values
(cost per unit of effect) at which to evaluate the curve.}
}
\value{
An \code{mb_ceac} object: a data-frame-like list with columns
\code{wtp}, \code{prob_cost_effective}, plus \code{n_draws} and \code{vintage}.
}
\description{
For a single A-vs-B comparison with sampled (\code{delta_cost},
\code{delta_effect}) draws (e.g. from a probabilistic sensitivity
analysis), returns the probability that B is cost-effective at
each willingness-to-pay (WTP) value in \code{wtp_grid}.
}
\details{
At each WTP value \code{lambda}, B is cost-effective if the
incremental net benefit \code{lambda * delta_effect - delta_cost > 0}.
The CEAC is the proportion of draws for which this is true.
}
\examples{
set.seed(4)
delta_cost   <- rnorm(1000, mean = 50000, sd = 10000)
delta_effect <- rnorm(1000, mean = 2,     sd = 0.5)
mb_ceac(delta_cost, delta_effect, wtp_grid = seq(0, 100000, by = 10000))
}
\references{
Fenwick, E., Claxton, K., Sculpher, M. (2001). Representing
uncertainty: the role of cost-effectiveness acceptability
curves. Health Economics 10(8). \url{doi:10.1002/hec.635}.
}
\seealso{
\code{\link[=mb_inb]{mb_inb()}}, \code{\link[=mb_icer]{mb_icer()}}.

Other cost-effectiveness: 
\code{\link{mb_cea}()},
\code{\link{mb_daly}()},
\code{\link{mb_icer}()},
\code{\link{mb_inb}()},
\code{\link{mb_qaly}()}
}
\concept{cost-effectiveness}
