Meta‐analysis of full ROC curves using bivariate time‐to‐event models for interval‐censored data

Meta‐analysis of full ROC curves using bivariate time‐to‐event models for... Systematic reviews and meta‐analyses are the cornerstones of evidence‐based medicine and inform treatment, diagnosis, or prevention of individual patients as well as policy decisions in health care. Statistical methods for the meta‐analysis of intervention studies are well established today. Meta‐analysis for diagnostic accuracy trials has also been a vivid research area in recent years, which is especially due to the increased complexity of their bivariate outcome of sensitivity and specificity. The situation is even more challenging when single studies report a full ROC curve with several pairs of sensitivity and specificity, each pair for a different threshold. Researchers frequently ignore this information and use only 1 pair of sensitivity and specificity from each study to arrive at meta‐analytic estimates. Although methods to deal with the full information have been proposed, they have some disadvantages, eg, the numbers or values of thresholds have to be identical across studies, or the precise values of thresholds are ignored. We propose an approach for the meta‐analysis of full ROC curves including the information from all thresholds by using bivariate time‐to‐event models for interval‐censored data with random effects. This approach avoids the problems of previous methods and comes with the additional advantage that it allows for various distributions of the underlying continuous test values. The results from a small simulation study are given, which show that the approach works well in practice. Furthermore, we illustrate our new model using an example based on the population‐based screening for type 2 diabetes mellitus. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Research Synthesis Methods Wiley

Meta‐analysis of full ROC curves using bivariate time‐to‐event models for interval‐censored data

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Publisher
Wiley Subscription Services, Inc., A Wiley Company
Copyright
Copyright © 2018 John Wiley & Sons, Ltd.
ISSN
1759-2879
eISSN
1759-2887
D.O.I.
10.1002/jrsm.1273
Publisher site
See Article on Publisher Site

Abstract

Systematic reviews and meta‐analyses are the cornerstones of evidence‐based medicine and inform treatment, diagnosis, or prevention of individual patients as well as policy decisions in health care. Statistical methods for the meta‐analysis of intervention studies are well established today. Meta‐analysis for diagnostic accuracy trials has also been a vivid research area in recent years, which is especially due to the increased complexity of their bivariate outcome of sensitivity and specificity. The situation is even more challenging when single studies report a full ROC curve with several pairs of sensitivity and specificity, each pair for a different threshold. Researchers frequently ignore this information and use only 1 pair of sensitivity and specificity from each study to arrive at meta‐analytic estimates. Although methods to deal with the full information have been proposed, they have some disadvantages, eg, the numbers or values of thresholds have to be identical across studies, or the precise values of thresholds are ignored. We propose an approach for the meta‐analysis of full ROC curves including the information from all thresholds by using bivariate time‐to‐event models for interval‐censored data with random effects. This approach avoids the problems of previous methods and comes with the additional advantage that it allows for various distributions of the underlying continuous test values. The results from a small simulation study are given, which show that the approach works well in practice. Furthermore, we illustrate our new model using an example based on the population‐based screening for type 2 diabetes mellitus.

Journal

Research Synthesis MethodsWiley

Published: Jan 1, 2018

Keywords: ; ; ;

References

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