R/ModelIPDnetmetareg.R

In htx-r/GenericModelNMA:

modelIPDnetmetareg <-function(){

  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 with treatment-by-covariate interactions - to estimate Odds Ratio (OR)
    logit(p[i]) <- u[studyid[i]]+theta[studyid[i],treat[i]]*(1-equals(treat[i],baseline[i]))+beta.0[studyid[i]]*x[i]+
      beta.w[studyid[i],treat[i]]*(x[i]-xbar[i])*(1-equals(treat[i],baseline[i]))+
      beta.b[studyid[i],treat[i]]*(1-equals(treat[i],baseline[i]))*xbar[i]
  }

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

    w[j,1]<- 0
    theta[j,t[j,1]]<- 0
    beta.w[j,t[j,1]] <- 0
    beta.b[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]]
      beta.w[j,t[j,k]] <- bw[t[j,k]] - bw[t[j,1]]
      beta.b[j,t[j,k]] <- bb[t[j,k]] - bb[t[j,1]]
    }
    }

  #** PRIORS

  # prior distribution for log-odds in baseline arm of study j
  for (j in 1:nIPD) {
    beta.0[j] ~ dnorm(0,.01)
    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
  bb[ref] <- 0
  bw[ref] <- 0
  for(k in 1:(ref-1)) {
    d[k] ~ dnorm(0,.01)
    bw[k]~dnorm(m.betaw,prec.betaw)
    bb[k]~dnorm(m.betab,prec.betab)
  }
  for(k in (ref+1):nt) {
    d[k] ~ dnorm(0,.01)
    bw[k]~dnorm(m.betaw,prec.betaw)
    bb[k]~dnorm(m.betab,prec.betab)
  }

   # priors for regression coefficients
    m.betaw~dnorm(0,1e-2)
    tau.betaw~dunif(0,2)
    tau.sq.betaw <- tau.betaw*tau.betaw
    prec.betaw <- 1/tau.sq.betaw

    m.betab~dnorm(0,1e-2)
    tau.betab~dunif(0,2)
    tau.sq.betab <- tau.betab*tau.betab
    prec.betab <- 1/tau.sq.betab

}

#