R/modelIPDnetmetaNRS.R

In htx-r/GenericModelNMA:

modelIPDnetmetaNRS <-function(){


  # IPD-NRS
  for (i in 1:np.NRS) { # loop through individuals

    # binomial likelihood of 0/1 bianry outcome y for each individual i of study j in arm k
    y.NRS[i]~dbern(p.NRS[i])

    # logistic transformation with treatment-by-covariate interactions - to estimate Odds Ratio (OR)
    logit(p.NRS[i]) <- u.NRS[studyid.NRS[i]]+theta.NRS[studyid.NRS[i],treat.NRS[i]]*(1-equals(treat.NRS[i],baseline.NRS[i]))
  }
  for(j in 1:(nIPD.NRS)) { # loop through IPD studies

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

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

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

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

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



  #** PRIORS
  #----- priors for NRS
  # prior distribution for log-odds in baseline arm of study j
  for (j in 1:(nIPD.NRS)) {u.NRS[j] ~ dnorm(0,.01)}

  # prior distribution for heterogeneity
  tau.NRS ~ dnorm(0,1)%_%T(0,)
  prec.NRS<- 1/pow(tau.NRS,2)

  # prior distribution for basic parameters
  d.NRS[ref.NRS] <- 0

  for(k in 1:(ref.NRS-1)) {
    d.NRS[k] ~ dnorm(0,.001)
  }
  for(k in (ref.NRS+1):nt.NRS) {
    d.NRS[k] ~ dnorm(0,.001)
  }

}
#