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## JAGS model for FE NMA with evidence consistency
nma.fe.c <- function(o){
out <- "
model{
for(i in 1:NS){
mu[i] ~ dnorm(0, 0.0001) # vague priors for trial baselines
for(k in 1:na[i]){
r[i,k] ~ dbin(p[i,t[i,k]], n[i,k]) # binomial likelihood
logit(p[i,t[i,k]]) <- mu[i] + md[i,t[i,k]] # model
rhat[i,k] <- p[i,t[i,k]]*n[i,k] # expected counts
dev[i,k] <- 2*(r[i,k]*(log(r[i,k]) - log(rhat[i,k])) +
(n[i,k] - r[i,k])*(log(n[i,k] - r[i,k]) - log(n[i,k] - rhat[i,k])))
# deviance contribution
}
resdev[i] <- sum(dev[i,1:na[i]]) # residual deviance for this trial
md[i,t[i,1]] <- 0
for(k in 2:na[i]){
md[i,t[i,k]] <- d[t[i,k]] - d[t[i,1]] # LOR
}
}
totresdev <- sum(resdev[]) # total residual deviance
d[1] <- 0
for(k in 2:NT){
d[k] ~ dnorm(0, 0.0001)
}
# pairwise ORs
for(c in 1:(NT - 1)){
for(k in (c + 1):NT){
lor[c,k] <- d[k] - d[c]
}
}
}"
return(out)
}
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