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

Nikoloulopoulos, Aristidis K. ORCID: https://orcid.org/0000-0003-0853-0084 (2018) Hybrid copula mixed models for combining case-control and cohort studies in meta-analysis of diagnostic tests. Statistical Methods in Medical Research, 27 (8). pp. 2540-2553. ISSN 0962-2802

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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 to 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.

Item Type:	Article
Uncontrolled Keywords:	generalized linear mixed model,composite likelihood,maximum likelihood,prevalnce
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Data Science and AI Faculty of Science > Research Groups > Statistics (former - to 2024) Faculty of Science > Research Groups > Numerical Simulation, Statistics & Data Science
Related URLs:	http://arxiv.org/abs/1604.05456
Depositing User:	Pure Connector
Date Deposited:	26 Apr 2016 13:00
Last Modified:	07 Nov 2024 12:39
URI:	https://ueaeprints.uea.ac.uk/id/eprint/58388
DOI:	10.1177/0962280216682376

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