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Chapter_12.txt
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Chapter_12.txt
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model {
# Binomial likelihood:
for (i in 1:12) {
r[i] ~ dbin(p[i], n[i])
}
# Declared relationships
# between basic and
# functional parameters:
p[1] <- a
p[2] <- b
p[3] <- c
p[4] <- d
p[5] <- (b * d + (1 - a - b) * e / (1 - a))
p[6] <- (a * c + b * d + (1 - a - b) * e)
p[7] <- (a * c * f) / ((a * c * f) + (b * d * g) + (e * h * (1 - a - b)))
p[8] <- (b * d * g) / ((b * d * g) + (e * h * (1 - a - b)))
p[9] <- ((f * c * a) + (g * d * b) + h * e * (1 - a - b)) /
((c * a) + (d * b) + e * (1 - a - b))
p[10] <- g
p[11] <- w
p[12] <- ((b * d) + (w * e) * (1 - a - b)) / ((b * d) + e * (1 - a - b))
# Estimated basic
# prior parameters:
a ~ dbeta(1, 2)
c ~ dbeta(1, 1)
d ~ dbeta(1, 1)
e ~ dbeta(1, 1)
f ~ dbeta(1, 1)
g ~ dbeta(1, 1)
h ~ dbeta(1, 1)
w ~ dbeta(1, 1)
z ~ dbeta(1, 1)
# Estiamted functional
# prior parameter:
b <- z * (1 - a)
# Distribution for NB of Maternal Diagnosis:
M <- 60012 - 54296 * Y
# Maternal diagnosis:
Y ~ dgamma(0.56, 3)T(0, 2)
# Net Benefit:
nb[1] <- 0
nb[2] <- 105000 * (1 - a - b) * (M * e * (1 - h) - 3.0 * (1 - e * h))
# EVPI:
nb.sorted <- sort(nb[])
vpi <- 7.7127 * (nb.sorted[N.k] - nb[k.current])
}