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 |