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Table 1 Overview of the 8 methods for pooling of cluster-specific concordance probability estimates

From: Assessing discriminative ability of risk models in clustered data

  Fixed effect meta-analysis Random effects meta-analysis
  Assuming the same true (logit) concordance probability within each cluster Assuming variation in true (logit) concordance probabilities across clusters
Probability scale   
Meta-analysis of cluster-specific estimates of the concordance probability 1. Equal weight for each cluster 6. Inverse of the sum of the cluster-specific sampling variance estimate and the between-cluster variance estimate
2. Number of subjects in the cluster
3. Number of subjects in the cluster with an event
4. Number of usable subject pairs within the cluster
5. Inverse of the cluster-specific sampling variance estimate
Log-odds scale   
Meta-analysis of cluster-specific estimates of the logit concordance probability 7. Inverse of the cluster-specific sampling variance estimate on log-odds scale 8. Inverse of the sum of the cluster-specific sampling variance estimate on log-odds scale and the between-cluster variance estimate on log-odds scale