An extended trivariate vine copula mixed model for meta-analysis of diagnostic studies in the presence of non-evaluable outcomes

TL;DR

This paper introduces an extended vine copula mixed model for meta-analysis of diagnostic studies, improving estimation accuracy over existing models especially when univariate effects are misspecified.

Contribution

It develops a flexible vine copula mixed model that encompasses the TGLMM and operates on original scales, enhancing meta-analytic estimation accuracy.

Findings

Vine copula model provides nearly unbiased estimates.

TGLMM overestimates when effects are misspecified.

Model successfully applied to coronary CT angiography studies.

Abstract

A recent paper proposed an extended trivariate generalized linear mixed model (TGLMM) for synthesis of diagnostic test accuracy studies in the presence of non-evaluable index test results. Inspired by the aforementioned model we propose an extended trivariate vine copula mixed model that includes the TGLMM as special case, but can also operate on the original scale of sensitivity, specificity, and disease prevalence. The performance of the proposed vine copula mixed model is examined by extensive simulation studies in comparison with the TGLMM. Simulation studies showed that the TGLMM overestimates the meta-analytic estimates of sensitivity, specificity, and prevalence when the univariate random effects are misspecified. The vine copula mixed model gives nearly unbiased estimates of test accuracy indices and disease prevalence. Our general methodology is illustrated by meta-analysing coronary CT angiography studies.