University of Leicester
Browse
Cerullo et al Research Synthesis Methods - 2022 - Meta‐analysis of dichotomous and ordinal tests with an imperfect gold.pdf (6.3 MB)

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

Download (6.3 MB)
journal contribution
posted on 2022-06-14, 09:13 authored by Enzo Cerullo, Hayley E Jones, Olivia Carter, Terry J Quinn, Nicola J Cooper, Alex J Sutton
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. This article is protected by copyright. All rights reserved.

History

Citation

Research synthesis methods, 2022, https://doi.org/10.1002/jrsm.1567

Author affiliation

Biostatistics Research Group, Department of Health Sciences, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

Research synthesis methods

Publisher

Wiley

issn

1759-2879

eissn

1759-2887

Acceptance date

2022-03-29

Copyright date

2022

Available date

2023-04-30

Spatial coverage

England

Language

eng

Usage metrics

    University of Leicester Publications

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC