Dear BUGS users,

I tried to fit a mixed treatment comparisons model together with a
meta-regression model. I have to compare 10 treatments, I dispose of 16
trials and I'd like to adjust for a variable representing the percentage of
patients with severe disease in each arm (e). 
I tried the following programm (the core programm is based on the one given
on the hsrc website : full random effect model) : 

model {
for (i in 1:ns) { 
 w[i,1] <- 0
 delta[i,t[i,1]] <- 0
 mu[i] ~ dnorm(0,.0001)
 for (k in 1:na[i]) {
   r[i,t[i,k]] ~ dbin(p[i,t[i,k]],n[i,t[i,k]])
   logit(p[i,t[i,k]]) <- mu[i] + delta[i,t[i,k]] + beta*(e[i,t[i,k]] -
 for (k in 2:na[i]) {
   delta[i,t[i,k]] ~ dnorm(md[i,t[i,k]],taud[i,t[i,k]])
   md[i,t[i,k]] <- d[t[i,k]] - d[t[i,1]] + sw[i,k]
   taud[i,t[i,k]] <- tau *2*(k-1)/k
   w[i,k] <- (delta[i,t[i,k]] - d[t[i,k]] + d[t[i,1]])
   sw[i,k] <- sum(w[i,1:k-1])/(k-1)

d[1] <- 0
for (k in 2:nt) { d[k] ~ dnorm(0,.0001) }
beta ~ dnorm(0.0,1.0E-6)

sd ~ dunif(0,2)
tau <- 1/pow(sd,2)

for (i in 1:ns) { mu1[i] <- mu[i]*equals(t[i,1],1) }
for (k in 1:nt) { logit(T[k]) <- sum(mu1[])/7 + d[k] }

for (c in 1:(nt-1)) { 
  for (k in (c+1):nt) { or[c,k] <- exp(d[k] - d[c]) }

data are under the form : 
r[,1]  n[,1] e[,1]  r[,2] n[,2]  e[,2] .... t[,1] t[,2] t[,3] na[]

the model doesn't compile and I don't know how to interprete the errors 

Any help would be very appreciated



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