Meta‐analysis of dichotomous and ordinal tests with an imperfect gold standard

Cerullo, E., Jones, H. E., Carter, O., Quinn, T. J. , Cooper, N. J. and Sutton, A. J. (2022) Meta‐analysis of dichotomous and ordinal tests with an imperfect gold standard. Research Synthesis Methods, 13(5), pp. 595-611. (doi: 10.1002/jrsm.1567) (PMID:35488506) (PMCID:PMC9541315)

Abstract

Standard methods for the meta-analysis of medical tests, without assuming a gold standard, are limited to dichotomous data. Multivariate probit models are used to analyze correlated dichotomous data, and can be extended to model ordinal data. Within the context of an imperfect gold standard, they have previously been used for the analysis of dichotomous and ordinal test data from a single study, and for the meta-analysis of dichotomous tests. However, they have not previously been used for the meta-analysis of ordinal tests. In this paper, we developed a Bayesian multivariate probit latent class model for the simultaneous meta-analysis of ordinal and dichotomous tests without assuming a gold standard, which also allows one to obtain summary estimates of joint test accuracy. We fitted the models using the software Stan, which uses a state-of-the-art Hamiltonian Monte Carlo algorithm, and we applied the models to a dataset in which studies evaluated the accuracy of tests, and test combinations, for deep vein thrombosis. We demonstrate the issues with dichotomising ordinal test accuracy data in the presence of an imperfect gold standard, before applying and comparing several variations of our proposed model which do not require the data to be dichotomised. The models proposed will allow researchers to more appropriately meta-analyse ordinal and dichotomous tests without a gold standard, potentially leading to less biased estimates of test accuracy. This may lead to a better understanding of which tests, and test combinations, should be used for any given medical condition.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Quinn, Professor Terry
• Authors: Cerullo, E., Jones, H. E., Carter, O., Quinn, T. J., Cooper, N. J., and Sutton, A. J.
• College/School: College of Medical Veterinary and Life Sciences > School of Cardiovascular & Metabolic Health
• Journal Name: Research Synthesis Methods
• Publisher: Wiley
• ISSN: 1759-2879
• ISSN (Online): 1759-2887
• Published Online: 30 April 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Research Synthesis Methods 13(5): 595-611
• Publisher Policy: Reproduced under a Creative Commons License

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
190914		SRP Complex Reviews Research Support Unit	Olivia Wu	National Institute for Health Research (NIHR)	14/178/29	HW - Health Economics and Health Technology Assessment