modelNMABinary=function(){
for(i in 1:ns) {
w[i,1]<- 0
theta[i,t[i,1]]<- 0
##binomial likelihood of number of events for each arm k of study i
for (k in 1:na[i]) {r[i,t[i,k]] ~ dbin(p[i,t[i,k]],n[i,t[i,k]])}
##parameterization of the 'true' effect of each comparison
##of arm k vs. baseline arm (1) of study i
logit(p[i,t[i,1]])<- u[i]
for (k in 2:na[i]) {
logit(p[i,t[i,k]])<- u[i] + theta[i,t[i,k]]
##distribution of random effects
theta[i,t[i,k]] ~ dnorm(md[i,t[i,k]],precd[i,t[i,k]])
## accounting for correlation between effect sizes estimated in multi-arm trials
md[i,t[i,k]]<- mean[i,k] + sw[i,k]
w[i,k]<- (theta[i,t[i,k]] - mean[i,k])
sw[i,k]<- sum(w[i,1:(k-1)])/(k-1)
precd[i,t[i,k]]<- prec *2*(k-1)/k
##consistency equations
mean[i,k] <-d[t[i,k]] - d[t[i,1]]
}}
##prior distribution for log-odds in baseline arm of study i
for (i in 1:ns) {u[i] ~ dnorm(0,.01)}
##prior distribution for heterogeneity
tau ~ dnorm(0,1)%_%T(0,)
prec<- 1/pow(tau,2)
tau.sq<- pow(tau,2)
##prior distribution for basic parameters
d[ref] <- 0
for(k in 1:(ref-1)) {d[k] ~ dnorm(0,.01)}
for(k in (ref+1):nt) {d[k] ~ dnorm(0,.01)}
##OR for each comparison
for(i in 1:(nt-1)) {
for (j in (i+1):nt) {
OR[j,i]<- exp(d[j] - d[i])
LOR[j,i]<- d[j] - d[i]}}
for(j in 1:(ref-1)){ORref[j]<- exp(d[j] - d[ref])}
for(j in (ref+1):nt) {ORref[j]<- exp(d[j] - d[ref])}
#Ranking of treatments#
}
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