R/ModelIPDADnetmeta.R

In htx-r/GenericModelNMA:

modelIPDADnetmeta <-function(){
  # IPD
  for (i in 1:np) { # loop through individuals

    # binomial likelihood of 0/1 bianry outcome y for each individual i of study j in arm k
    y[i]~dbern(p[i])
    # logistic transformation - to estimate Odds Ratio (OR)
    logit(p[i]) <- u[studyid[i]]+theta[studyid[i],treat[i]]*(1-equals(treat[i],baseline[i]))
  }

  for(j in 1:nIPD) { # loop through IPD studies

     w[j,1]<- 0
     theta[j,t[j,1]]<- 0

     for (k in 2:na[j]){ # loop through non-referent IPD arms

      # distribution of random effects
      theta[j,t[j,k]] ~ dnorm(md[j,t[j,k]],precd[j,t[j,k]])

      # accounting for correlation between effect sizes estimated in multi-arm trials
      md[j,t[j,k]]<- mean[j,k] + sw[j,k]
      w[j,k]<- (theta[j,t[j,k]]  - mean[j,k])
      sw[j,k]<- sum(w[j,1:(k-1)])/(k-1)
      precd[j,t[j,k]]<- prec *2*(k-1)/k

      # consistency equations
      mean[j,k] <-d[t[j,k]] - d[t[j,1]]

    }}

  # AD
  for (j in (nIPD+1):(nAD+nIPD)) { # loop through AD studies

    w[j,1]<- 0
    theta[j,t[j,1]]<- 0

    for (k in 1:na[j]) { # loop through AD arms

      # binomial likelihood of number of events for each study j in arm k
      r[(j-nIPD),t[j,k]] ~ dbin(pa[j,t[j,k]],n[(j-nIPD),t[j,k]])
    }

    # referent arm
    logit(pa[j,t[j,1]]) <- u[j]

    for (k in 2:na[j]){ # loop through non-referent AD arms

      # logistic transformation with treatment-by-covariate interactions in study-level - to estimate Odds Ratio (OR)
      logit(pa[j,t[j,k]]) <- u[j]+theta[j,t[j,k]]

      # distribution of random effects
      theta[j,t[j,k]] ~ dnorm(md[j,t[j,k]],precd[j,t[j,k]])

      # accounting for correlation between effect sizes estimated in multi-arm trials
      md[j,t[j,k]]<- mean[j,k] + sw[j,k]
      w[j,k]<- (theta[j,t[j,k]]  - mean[j,k])
      sw[j,k]<- sum(w[j,1:(k-1)])/(k-1)
      precd[j,t[j,k]]<- prec *2*(k-1)/k

      # consistency equations
      mean[j,k] <-d[t[j,k]] - d[t[j,1]]
    }
}

  #** PRIORS

  # prior distribution for log-odds in baseline arm of study j in IPD and AD
  for (j in 1:(nIPD+nAD)) {u[j] ~ 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]}}


  }

#