Hybrid copula mixed models for combining case-control and cohort studies in meta-analysis of diagnostic tests

TL;DR

This paper introduces a hybrid copula mixed model that combines data from case-control and cohort studies in meta-analysis of diagnostic tests, improving data utilization and accounting for study design and dependence structures.

Contribution

The paper proposes a novel hybrid copula mixed model that integrates bivariate and trivariate models to effectively synthesize diverse study types in diagnostic test meta-analyses.

Findings

Successfully applied to melanoma metastases imaging data

Enhanced data utilization by combining study designs

Model captures dependence in joint tails

Abstract

Copula mixed models for trivariate (or bivariate) meta-analysis of diagnostic test accuracy studies accounting (or not) for disease prevalence have been proposed in the biostatistics literature to synthesize information. However, many systematic reviews often include case-control and cohort studies, so one can either focus on the bivariate meta-analysis of the case control studies or the trivariate meta-analysis of the cohort studies, as only the latter contains information on disease prevalence. In order to remedy this situation of wasting data we propose a hybrid copula mixed model via a combination of the bivariate and trivariate copula mixed model for the data from the case-control studies and cohort studies, respectively. Hence, this hybrid model can account for study design and also due its generality can deal with dependence in the joint tails. We apply the proposed hybrid copula mixed model to a review of the performance of contemporary diagnostic imaging modalities for detecting metastases in patients with melanoma.