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A new method for synthesizing test accuracy data outperformed the bivariate method

Version 2 2023-10-19, 04:55
Version 1 2023-09-18, 12:46
journal contribution
revised on 2023-10-19, 04:54 and posted on 2023-10-19, 04:55 authored by Luis Furuya-Kanamori, Polychronis Kostoulas, Suhail A.R. Doi

Objectives

This study outlines the development of a new method (split component synthesis; SCS) for meta-analysis of diagnostic accuracy studies and assesses its performance against the commonly used bivariate random effects model.

Methods

The SCS method summarizes the study-specific diagnostic odds ratio (on the ln(DOR) scale), which mainly reflects test discrimination rather than threshold effects, and then splits the summary ln(DOR) into its component parts, logit sensitivity (Se) and logit specificity (Sp). Performance of SCS estimator was assessed through simulation and compared against the bivariate random effects model estimator in terms of bias, mean squared error (MSE), and coverage probability across varying degrees of between-studies heterogeneity.

Results

The SCS estimator for the DOR, Se, and Sp was less biased and had smaller MSE than the bivariate model estimator. Despite the wider width of the 95% confidence intervals under the bivariate model, the latter had a poorer coverage probability than that under the SCS method.

Conclusion

The SCS estimator outperforms the bivariate model estimator and thus represents an improvement in the approach to diagnostic meta-analyses. The SCS method is available to researchers through the diagma module in Stata and the SCSmeta function in R.

Other Information

Published in: Journal of Clinical Epidemiology
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.jclinepi.2020.12.015

Funding

Open Access funding provided by the Qatar National Library

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2021

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Qatar University
  • Qatar University Health - QU
  • College of Medicine - QU HEALTH

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