#  9.1   Ana:  cost-effectiveness analysis

model
{
mu.e<-0.28
prec.e <- 1/(0.123*0.123)
theta.e ~ dnorm(mu.e, prec.e)

mu.c <- 1380 + .34*5657*(theta.e-0.28)/0.123
prec.c <- 1/( 5657*5657*(1-.34*.34))
theta.c ~ dnorm(mu.c, prec.c)

ICER<-theta.c/theta.e
#                work out quadrant *(very messy! must be neater way)
s.c<-step(theta.c)
s.e<-step(theta.e)
binary<-s.c+s.e*2+1  #  takes on values 4,2,1,3 for quadrants I,II,III,IV
recode[1] <- 3; recode[2] <- 2; recode[3]<-4; recode[4]<-1
for(i in 1:4){
quad[i]<- equals(recode[binary],i)    #  quadrant
rho[i] <- equals(recode[binary],i)*ICER #  this does not give conditional ICER
}
#   CEAC curves
for(j in 1:21){
K[j]<- (j-1)*5000
INB[j] <- K[j]*theta.e - theta.c
Q[j] <- step( INB[j] )  #   
}
}