Proposes and compares CG-C, 2MA-C and MIX-C random-effects approaches for clustered flexible calibration plots, with simulation and case-study evidence favoring 2MA-C (splines) for overall curves and MIX-C for cluster-specific curves.
The inclusion of the estimated inter-study variation into forest plots for random effects meta-analyses – a suggestion for a graphical representation
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Clustered Flexible Calibration Plots For Binary Outcomes Using Random Effects Modeling
Proposes and compares CG-C, 2MA-C and MIX-C random-effects approaches for clustered flexible calibration plots, with simulation and case-study evidence favoring 2MA-C (splines) for overall curves and MIX-C for cluster-specific curves.