#REF: https://CRAN.R-project.org/package=BCEA
# add tutorial data
if(0==1){
incremental_cost <- sim_data$incremental_cost_disc
incremental_effect <- sim_data$total_DALYs_averted_disc
course_of_action <- sim_data$scenario_name
}
library('BCEA')
bcea_ce_plane <- function(incremental_cost,incremental_effect,course_of_action) {
opt_interventions <- unique(course_of_action)
num_interventions <- length(opt_interventions)
num_sim <- length(incremental_cost) / num_interventions
e_bcea <- matrix(0,nrow = num_sim, ncol=num_interventions)
c_bcea <- matrix(0,nrow = num_sim, ncol=num_interventions)
i <- 1
for(i in 1:num_interventions){
flag <- course_of_action == opt_interventions[i]
e_bcea[,i] <- incremental_effect[flag]
c_bcea[,i] <- incremental_cost[flag]
}
# add current
e_bcea <- cbind(e_bcea,0)
c_bcea <- cbind(c_bcea,0)
opt_interventions <- c(opt_interventions,"current")
pdf('bcea_ceplane.pdf')
sim_bcea <- bcea(e = -e_bcea,
c = -c_bcea,
ref = 4,
interventions = opt_interventions,
plot = TRUE)
ceplane.plot(sim_bcea, # plots the Cost-Effectiveness plane
wtp=25000, # selects the relevant willingness to pay (default: 25,000)
graph="ggplot2",
ICER.size=2
)
contour(sim_bcea,
comparison = 1:3,
graph = "ggplot2"
)
contour2(sim_bcea,
comparison = 1:3,
xl=c(0,6000),
yl=c(3000000,21000000),
ICER.size=2,
graph = "ggplot2"
)
ceac.plot(sim_bcea,
comparison = 1:3,
graph="ggplot2"
)
names(sim_bcea)
sim_bcea$ICER
x <- multi.ce(sim_bcea)
names(x)
x$m.ce
x$interventions
head(x$k)
head(x$ceaf)
head(x$n.comparators)
plot(x$ceaf)
plot(x$m.ce[,4],type='l')
x <- ceaf.plot(multi.ce(sim_bcea),
graph="ggplot2"
)
x$data$k
dev.off()
}
dim(e_bcea)
dim(c_bcea)
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