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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 analyse 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 article, 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.
| Original language | English |
|---|---|
| Pages (from-to) | 595-611 |
| Number of pages | 17 |
| Journal | Research Synthesis Methods |
| Volume | 13 |
| Issue number | 5 |
| Early online date | 30 Apr 2022 |
| DOIs | |
| Publication status | Published - 11 Sept 2022 |
Bibliographical note
Funding Information:The work was carried out whilst EC was funded by a National Institute for Health Research (NIHR) Complex Reviews Support Unit (project number 14/178/29) and by a National Institute for Health Research Systematic Review Fellowship (project number RM‐SR‐2017‐09‐023). The views and opinions expressed herein are those of the authors and do not necessarily reflect those of the NIHR, NHS or the Department of Health. The NIHR had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. This project is funded by the NIHR Applied Research Collaboration East Midlands (ARC EM). The views expressed are those of the authors and not necessarily those of the NIHR or the Department of Health and Social Care.
Publisher Copyright:
© 2022 The Authors. Research Synthesis Methods published by John Wiley & Sons Ltd.
Keywords
- imperfect gold
- latent class
- meta-analysis
- multivariate probit
- ordinal tests
- test accuracy
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Bristol TAG
Whiting, P. F. (Co-Principal Investigator), Welton, N. J. (Co-Principal Investigator), Owen-Smith, A. L. (Co-Investigator), Phillippo, D. M. (Co-Investigator), Caldwell, D. (Co-Investigator), Marques, E. M. R. (Co-Investigator), Jones, H. E. (Co-Investigator), Thom, H. H. Z. (Co-Investigator), Pedder, H. (Co-Investigator), Savovic, J. (Co-Investigator), Higgins, J. P. T. (Co-Investigator), Sadek, A. (Co-Investigator), Lopez Manzano, C. (Co-Investigator), Cooper, C. (Co-Investigator), Tomlinson, E. (Co-Investigator), Carroll, J. S. (Co-Investigator), Walker, J. G. (Co-Investigator), Ward, M. E. (Co-Investigator) & James, R. M. (Co-Investigator)
1/04/22 → 31/03/27
Project: Research, Parent
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